Rebound Effect of Efficiency Improvement in ...

Rebound Effect of Efficiency Improvement in Passenger Cars on Gasoline Consumption in Canada

Saeed Moshiri Department of Economics, STM College, University of Saskatchewan 1437 College Drive, Saskatoon, SK, Canada S7N 0W6 [email protected] Kamil Aliyev Department of Economics, University of Saskatchewan 1437 College Drive, Saskatoon, SK, Canada S7N 0W6 [email protected]

Highlights    

The rebound effect of fuel efficiency in vehicle transportation is estimated. The Canadian household spending data and the AIDS model are used. The average rebound effect is between 82 and 88 percent. The rebound effect varies in provinces and rises with income and gasoline prices.

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Rebound Effect of Efficiency Improvement in Passenger Cars on Gasoline Consumption in Canada

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Abstract The fossil fuel-driven transport sector has been one of the major contributors to CO2 emission across the world, keeping it on the energy policy agenda for the past three decades. Canada ranks second in gasoline consumption among OECD countries, and Canadian gasoline expenditure share has been increasing since the 1990s. Fuel efficiency policies aim to decrease gasoline consumption; however, the effect can be mitigated by changes in consumer behaviour such as traveling more distances — a rebound effect. Thus, the effectiveness of fuel efficiency policy is dependent on the magnitude of the rebound effect. In this paper, we estimate rebound effect for personal transportation in Canada using the data from the Household Spending Survey for the period 1997-2009. The model includes a system of expenditure share equations for gasoline, other energy, and non-energy goods specified by AIDS and QUAIDS models and estimated by the nonlinear SUR method. Our estimation results show a rather high rebound effect of 82-88 percent on average in Canada, but with a great heterogeneity across income groups and provinces. Specifically, the rebound effect ranges from 63 to 96 percent across income groups and provinces and increases with gasoline prices. Keywords: Gasoline, demand, AIDS, QUAIDS, rebound effect, Canada JEL Classification: D1, Q41, Q48

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1. Introduction The rise in oil prices in the 2000s, along with increasing concerns about the greenhouse gas (GHG) emissions, spurred policy makers and the auto-manufacturing sector to adopt more stringent fuel efficiency standards. Fuel efficient cars use less gas per kilometer, resulting in saving on fuel cost and reduction of GHG emissions. However, the new fuel efficiency standards may also trigger rebound effect: a tendency to increase distance traveled or to switch to larger vehicles, offsetting some of the initial gains on fuel costs and emission reduction. High rebound effect implies that an increase in efficiency itself cannot achieve the desired targets for emission reduction and should, therefore, be coupled with other policies to dis-incentivize gasoline consumption. The magnitude of the rebound effect is thus critical for the proper policy design on reducing gasoline consumption and emissions. Fuel efficiency can have multiple rebound effects on gasoline consumption.1 The direct effect arises from increased energy use induced by the reduction of fuel cost due to higher efficiency. A secondary effect is associated with an increase in consumption of all other goods and services whose production requires energy. There is also an economy-wide and international effect which concerns changes in labour market and international trade and their overall effects on the aggregate output and energy consumption. Finally, fuel efficiency may bring about changes in consumer tastes, which may have an impact on energy consumption. Canada produces about 2% of global greenhouse gas emission, while having only 0.5% of the world's population. Based on OECD ranking for high-income countries, Canada is second highest in terms of gasoline consumption per capita, and its CO2 emissions from fuel combustion have increased by about 24 percent between 1990 and 2010. Emissions from transportation are the

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See Turner (2013) for a critical view on different classifications of rebound effect.

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largest contributor to Canada’s GHG emission with about 75 percent of oil-related GHG emission coming from fuel used by vehicles (Environment Canada, 2013). To curb the GHG emission, Canada has adopted fuel efficiency standards such as the 2010 Passenger Automobile and Light Truck Greenhouse Gas Emission Regulation (LDV1), according to which the fuel efficiency of new passenger light trucks is expected to increase by 37 percent, decreasing gasoline consumption of new cars from 8.6 l/100 km in 2010 to 6.4 L/100 km in 2020. Canada is also a geographically large country with a low density population and heterogeneous provinces in terms of economic activities, energy consumption, and emission levels. Since natural resources and energy management as well as environmental policies are primarily under provincial jurisdictions, studies at the provincial level will shed more light on the dynamics of energy demand and rebound effects in Canada. Energy demand is also heterogeneous across income groups, leading to different rebound effects in low and high income groups. Low income households spend more on energy relative to their income than high income households, but higher income households can better afford to switch to larger cars when gasoline prices fall. Rebound effects are also expected to increase with energy prices, as fuel efficiency will generate more savings when prices are higher. There are many studies on rebound effects in different sectors in OECD countries, but the number of studies in Canada, particularly at the micro level, is limited. The household spending data overcomes the shortcomings associated with the small sample size in aggregate level studies and allows for examining the heterogeneity in income effects and controlling for the impact of demographical changes. The household spending survey data also allows us to incorporate the interaction between various energy commodities and non-energy goods in the consumption basket, an option which is not available in the partial demand models using the transportation survey data.

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In this paper, we estimate demand for gasoline and direct rebound effect in Canada for three income groups and nine provinces using the Canadian Survey of Household Spending data for the period 1997-2009. Our estimation results show that demand for gasoline in Canada is inelastic, and the rebound effect is significant and higher than the average effect in OECD countries. The results also indicate great heterogeneities in elasticities and the rebound effects in Canadian provinces and income groups and at different gasoline price levels. Specifically, the rebound effect is higher in high income families and provinces than in low income families and provinces, and increases with gasoline prices. The rest of the paper is organized as follows: Section 2 reviews the literature and Section 3 discusses the theoretical background. Sections 4 -6 present and discuss the data the results and Section 7 draws conclusion.

2. Review of Previous Studies Studies on rebound effect started in the early 1980s, but only recently has the topic received growing interest in academic and policy circles. Theoretical and particularly empirical papers on rebound effects are now numerous with a wide range in reported results. Researchers have used a variety of models, econometric techniques, data types, and time periods to estimate rebound effect in different sectors. However, the variation among studies in the empirical results on the rebound effect of the fuel efficiency can be ascribed mainly to the data types used for estimation, which are based on either transportation surveys or household budget surveys. Table 1 presents summary of the selected studies on fuel efficiency rebound effect for OECD countries. [Table 1 here]

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There have been also some reviews of the literature summarizing the hugely varying estimates of the rebound effect in different countries. The earlier studies focus mainly on the price elasticity of gasoline, which can be used to derive the rebound effect. For instance, Goodwin (1992) reviews more than 50 studies and reports the price elasticity of fuel consumption in road traffic within a range of -0.27 to -0.73 in the short-run and long-run, respectively. Espey (1998) builds on the previous reviews and concludes almost the same ranges for the price elasticities, but lower values for the long-run. Graham and Glaister (2002), however, report a higher value (-0.8) for long-run price elasticity. In more recent studies, Goodwin et al. (2004) and Graham and Glaister (2004) carry out two parallel blind reviews of 69 studies on road traffic and fuel consumption in OECD countries covering periods from 1929 to 1998. The former reports price elasticities of fuel consumption within a range of -0.25 to -0.60 and income elasticity within a range of 0.39 and 1.08 in the short-run and long-run, respectively. The reported results are similar in the latter. More recent studies also report different results on price elasticity of gasoline and rebound effects, depending on type of the data used. Overall, studies that use aggregate data tend to report a lower price elasticity and implied rebound effect than those that use household budget survey data. For instance, the rebound effects obtained from national or state/provincial level data by Matos et al. (2011) for Portugal (1987-2006), Brännlund et al. (2007) for Sweden (1980-1997), Small and Van Dender (2007) for US (1966-2001), and Barla et al. (2009) for Canada (1990-2004) are in the range of 5 to 50 percent. However, the household level studies by West (2004) for US (1997), Frondel et al. (2008) for Germany (1997-2009), and Chitnis et al. (2014) for UK report rebound effects in the range of 25 to 87 percent.

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3. Theoretical Background A utility maximizer consumer will decide how much to consume of different goods and services, given a preference structure, disposable income, and the prices. In our context, the consumer basket includes three goods: gasoline, other energies, and non-energy goods. The total effect of the improvement in fuel efficiency on gasoline consumption can be divided into two parts: price or substitution effect and income effect. The former implies that the consumer will purchase more gasoline as fuel cost is cheaper than other energy and non-energy goods, and the latter means that the consumer has more income to spend on gasoline and other goods and receives a higher level of utility. The direct rebound effect refers to changes in gasoline consumption through both substitution and income effects of fuel efficiency. The indirect (secondary) rebound effect arises from changes in consumption of other energy and non-energy goods due to the income effect of fuel efficiency, which may increase total energy consumption. See Appendix A for a more detailed description of the rebound effect. Rebound effect of fuel efficiency is defined as a relative change in gasoline consumption as a result of a relative change in fuel efficiency, or the elasticity of gasoline consumption (G) with respect to fuel efficiency (𝜀): 𝜂𝜀 (𝐺) =

𝜕𝐺 𝜀 𝜕𝜀 𝐺

,

(1)

Fuel efficiency is defined as a ratio of energy output, the service produced by energy, to energy input. In transportation, energy input is gasoline (G) and energy output is vehicle distance travelled (T). Therefore, 𝜀=

𝑇 𝐺

(2)

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Given the prices, an increase in fuel efficiency will decrease fuel cost per distance traveled, which may lead to traveling more distances or switching to larger cars—the rebound effect. The rebound effect can also be defined as a relative change in output (vehicle distance travelled) due to a relative change in fuel efficiency, or the elasticity of energy output (T) with respect to fuel efficiency (𝜀): 𝜂𝜀 (𝑇) =

𝜕𝑇 𝜀 𝜕𝜀 𝑇

(3)

The rebound effect implies that |𝜂𝜀 (𝐺)| < 1 or |𝜂𝜀 (𝑇)| > 0. Substituting for G from (2) into (1) will yield the relationship between the fuel efficiency elasticities of energy input and energy output as follows (Khazzoom, 1980): 𝜂𝜀 (𝐺) = −[1 + 𝜂𝜀 (𝑇)]

(4)

Equation (4) suggests that the total changes in gasoline demand (G) come from two sources: a change in fuel efficiency, which is equal to -1, and a change in demand for energy service (−𝜂𝜀 (𝑇)). When |𝜂𝜀 (𝑇)| is non-zero, |𝜂𝜀 (𝐺)| will be less than one, indicating that the expected energy savings from higher efficiency will not be proportional, implying a rebound effect. However, if demand for energy output does not increase as a result of improving efficiency, i.e., 𝜂𝜀 (𝑇) = 0 , the efficiency and the demand for energy input will be proportional, implying a zero rebound effect. The rebound effect can be estimated from equation (4), which requires an estimation of the efficiency elasticity of energy services using a demand model. However, since efficiency is not observed directly, it can be proxied by the fuel cost, which is assumed to be proportional to

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efficiency (𝑃𝑇 =

𝑃𝐺 𝜀

). Most studies estimate the vehicle distance travelled to obtain the fuel

efficiency elasticity of energy output as a direct measure of rebound effect. Alternatively, the demand for gasoline can be used to estimate the price elasticity of energy input as a measure of rebound effect. Under the assumptions that fuel efficiency is exogenous and energy input and energy output and their prices are proportional, the two elasticities are the same: 𝜂𝑝𝑇 (𝑇) = 𝜂𝑝𝐺 (𝐺). We use the Almost Ideal Demand System (AIDS) developed by Deaton and Maellbauer (1980) to estimate demand for gasoline and price elasticity of demand in Canada using the household spending survey data. The model intends to link consumer behavior theory to the data by estimating a set of expenditure share equations and is widely used in the empirical literature. However, due to its linearity, the model is not capable of capturing the curvature of Engel curve. We will also use the Quadratic Almost Ideal Demand System (QUAIDS), which includes a quadratic term in the logarithm of expenditure whose coefficient varies with price and allows goods to be luxuries or necessities at different levels of expenditures (Banks et al., 1997). The cost minimization process of the consumers, given the preferences and prices, generates the following system of expenditure share equations: 2 𝑦

𝑠𝑖 = 𝛼𝑖 + ∑𝑛𝑗 ϒ𝑖𝑗 log 𝑝𝑗 + 𝛽𝑖 log {𝑎(𝑝)}.

(5)

where 𝑠𝑖 is the expenditure share for good i, 𝑝𝑗 is the price of good j (j=1,…, n), and y represents income. 𝑎(𝑝) and 𝑏(𝑝) are positive linearly homogeneous functions, which correspond to the cost of subsistence and bliss levels, respectively. They have a flexible functional form, so they can

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See Appendix B for detailed description of the model.

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reproduce any arbitrary set of the first and second order derivatives of the cost function at any single point. 𝑙𝑛 𝑎(𝑝) = 𝑎0 + ∑𝑛𝑖=1 𝛼𝑖 log 𝑝𝑖 +

1 2

∑𝑛𝑖=1 ∑𝑛𝑗=1 ϒ∗ 𝑖𝑗 log 𝑝𝑖 log 𝑝𝑗

(6)

𝛽

𝑏(𝑝) = log 𝑎(𝑝) + 𝛽0 ∏𝑛𝑖=1 𝑝𝑖 𝑖 The following restrictions apply to ensure the consistency with the consumer theory: Slutsky symmetry (ϒ𝑖𝑗 = ϒ𝑗𝑖 , 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖, 𝑗) , homogeneity of the Marshallian demand functions of degree zero in prices and income ( ∑𝑛𝑗=1 ϒ𝑖𝑗 = 0 , 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑖), and adding up condition (∑𝑛𝑖=1 𝛼𝑖 = 1, ∑𝑛𝑖=1 ϒ𝑖𝑗 = 0, ∑𝑛𝑖=1 𝛽𝑖 = 0). The QUAIDS model is similar to the AIDS model, but leads to an additional quadratic term for log of real income in the expenditure share equations as follows: 𝑦

𝜆

𝑦

2

𝑖 𝑠𝑖 = 𝑎𝑖 + ∑𝑛𝑖=1 ϒ𝑖𝑗 𝑙𝑛 𝑝𝑗 + 𝛽𝑖 ln [𝑎(𝑝)] + 𝑏(𝑝) {𝑙𝑛 [𝑎(𝑝)]}

(7)

where all variables are the same as defined before and all the restrictions in addition to ∑𝑛𝑖=1 λ𝑖 = 1 apply. The share equations can be used to derive the price and income elasticities of demand for different goods. The Marshalian uncompensated price elasticity and income elasticity for AIDS model are as follows (see Appendix C for details): 𝑢 𝑒𝑖𝑗 =

𝑒𝑖 =

𝜇𝑖𝑗 𝑠𝑖 𝜇𝑖 𝑠𝑖

− δ𝑖𝑗

+ 1.

(8)

(9)

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where

𝜇𝑖 =

delta (δ𝑖𝑗 = {

𝜕𝑠𝑖

= 𝛽𝑖 , 𝜇𝑖𝑗 = 𝜕 ln 𝑦

𝜕𝑠𝑖 𝜕 ln 𝑝𝑗

= ϒ𝑖𝑗 − 𝜇𝑖 (𝛼𝑗 + ∑𝑘 ϒ𝑖𝑗 𝑙𝑛 𝑃𝑘 ), and 𝛿 is the Kronecker

0 𝑖𝑓 𝑖 ≠ 𝑗 ). 1 𝑖𝑓 𝑖 = 𝑗

The uncompensated price elasticities include both substitution and income effects of price changes. The compensated price elasticity measures the effect of the price changes on demand when a consumer is compensated for the income effect of price changes. The relationship between the uncompensated and compensated price elasticities is as follows: 𝑢 𝑐 𝑒𝑖𝑗 − 𝑒𝑖𝑗 = − 𝑒𝑖 𝑠𝑗 .

(10)

where “u” and “c” stand for uncompensated and compensated, and -eisj is the income effect. The price elasticities above offer different measures of rebound effect. eiic measures the own substitution (price) effect and -eisi the pure income effect on gasoline demand. We can also measure the effect of the fuel efficiency on demand for other goods by obtaining the cross-price elasticities. Specifically, eijc measures the substitution effect of changes in gasoline prices on the demand for other goods, and -eisj measures the income effect of changes in gasoline prices on other goods.

4. Data We use the Canadian Survey of Household Spending published by Statistics Canada and available for the period 1997-2009. The total sample includes 47,921 observations from 9 federated provinces, which we divide in three income groups nationally and provincially by taking

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three quantiles (low, mid, high)3. The household members in the sample are aged 25 to 64 living in urban areas.4 The Summary statistics of the data are presented in Table 2. The expenditure shares are constructed by dividing corresponding expenditures by the total annual expenditures of the household. The average gasoline and other energy expenditure shares in Canadian households are about 3 percent. Price for other energy is constructed as the weighted average of electricity, natural gas, and other fuels. Weights are obtained by dividing corresponding expenditures by total expenditures on other energy. The Consumer Price Index, excluding energy prices, is used for prices of other goods and services. Some demographic variables, such as number of children under 17 and number of vehicles per adult in a household, are also added to the model to control for household characteristics. Households having more children and a larger number of vehicles per adult are expected to have higher gasoline expenditure. [Table 2 here] Figure 1 shows that gasoline expenditure increases with income and over time, and Figure 2 shows that gasoline expenditure shares decrease with income and have a positive trend for the low and mid-income households. This suggests that the income elasticity of demand should be positive and less than one. [Figures 1 & 2 here]

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Canada has 10 federated provinces as follows: Alberta (AB), British Columbia (BC), Manitoba (MB), New Brunswick (NB), Newfoundland and Labrador (NL), Nova Scotia (NS), Ontario (ON), Prince Edward Island (PEI), Quebec (QC), Saskatchewan (SK). PEI was excluded from the analysis because of the small number of observations and masked records for urban or rural. 4 The top and bottom 5 percent of observations are cut to avoid outliers. The excluded observations include extremely low or high gasoline or total expenditures. Extreme high values of gasoline expenditures may be due to usage of the private car for commercial purposes. The very low values make shares extremely low, generating unreasonable elasticities.

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Figure 3 presents the average gasoline price and the gasoline expenditure shares for the Canadian households, and Figure 4 shows the gasoline price trends in provinces for the period 1997-2009. Overall, gasoline prices and gasoline expenditure shares have been increasing, and prices vary across provinces, with Newfoundland and Labrador (NL) having the highest prices and Alberta (AB) the lowest. [Figures 3 - 4 here] 5. Estimation Results We estimate a system of expenditures shares equations (5) and (7) for gasoline, other energy (electricity, natural gas, and others), and non-energy goods using AIDS and QUAIDS models to obtain the rebound effect of fuel efficiency in the passenger cars in Canada. The models are estimated by the non-linear Seemingly Unrelated Regression Model (NLSURE), which uses an iterative feasible generalized least squares (FGNLS) method to estimate a system of non-linear equations jointly. The standard errors are robust to heteroskedasticity and serial correlation and the sample weights are applied.5 We have also added a trend term to the regression equations to capture the possible changes in tastes during the sample period. Given the prices, income, and other household characteristics, people may travel by car more or less depending on environmental concerns, changes in social conditions, and preferences on recreational activities. Using the estimated elasticities, the rebound effect is calculated from equation (4). Because of the focus of our study and space limit, we report the results only for gasoline, but the estimation results for other two equations are also available upon request.

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We make use of the Stata codes developed by Poi (2010), but modify it according to our data and specification.

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The sign of the income variable in the expenditure share equation depends on relative changes in gasoline versus other goods expenditures. Higher income will increase spending on other goods and gasoline; however, it is expected that expenditures on other goods will be relatively more than on gasoline, particularly for high-income households. Therefore, the effect of income on gasoline expenditure share is expected to be negative. Gasoline price is expected to have a positive impact on the gasoline expenditure share since it is considered a necessity commodity. Vehicle per adult ratio is expected to have a positive sign as households with more cars per adult tend to use cars more often and therefore spend more on gasoline. Number of children is also expected to have a positive sign, since having a child in the family is associated with more driving for child related activities. The estimation results for the full sample are presented in Table 3. All coefficients in the AIDS model have expected signs and are statistically significant. Specifically, the coefficient of income is negative and gasoline price positive. The former implies that gasoline is a normal and necessary commodity, and the latter suggests that demand for gasoline is inelastic with respect to price changes. The coefficients of price of other energy is positive and price of non-energy goods negative. Number of vehicles per adult and number of children have a positive effect on gasoline expenditure share and the trend coefficient indicates that Canadians have been spending relatively more on gasoline in the study period. The coefficients in the QUAIDS model are almost the same as those in the AIDS model, but all prices have smaller effect and the price of other energy goods is not significant. λ, which represents the effect of the quadratic term of logarithm of income, is very small and statistically not significant. [Table 3 here]

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Household expenditures may be heterogeneous with respect to income level. Therefore, we estimate the model for three income groups (low, mid, and high) separately. Table 4 presents results for AIDS model.6 The coefficient for income is negative and about the same for all three income groups. Price coefficients are positive and significant but greater for low and mid income groups. The coefficient of prices of other energy goods is negative and not significant for lowincome households, but positive and significant for the other income groups. The coefficients of the price of non-energy goods are negative across all income groups, but smaller for the highincome households. Vehicle per adult coefficients are positive and statistically significant for all income groups, but the effect in mid- and high-income households are twice as much as that in low-income households. The impact of number of children on gasoline expenditure share is also positive and significant for all income groups, but verey small for high-income households. The trend effect is positive and significant for mid- and high-income households. [Table 4 here] Table 5 presents results for income, own price, and efficiency elasticities of gasoline demand estimated from AIDS model using equations (8) and (9) and evaluated at the sample mean for Canada, income groups, and provinces. The nonlinear standard errors are obtained using the Delta method. Income elasticities are positive, less than one, and significant in Canada and across income groups and provinces, consistent with our expectation that gasoline is a normal and necessity good. The average income elasticity in Canada is 0.47 and decreases with income from 0.55 for low income families to 0.33 for high income families, indicating that lower income families increase their gasoline consumption more relative to high income families as their income

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Since the results from the QUAIDS model are similar to the AIDS model, we do not report them here, but they are available upon request.

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rises. The income elasticities also vary in provinces, with NB having the lowest elasticity (0.40) and SK and QC the highest (0.50). The price elasticities are all negative, significant, and less than one, implying an inelastic demand for gasoline. The average price elasticity in Canada is -0.88, but rises (in absolute value) with income level from -0.86 to -0.94 for low and high income families, respectively. The provincial price elasticities also vary from -0.63 in NS to -0.96 in BC. The efficiency elasticities are presented in Column 3. The elasticity is -0.12 on average and decreases with income. Provincial estimation results also show that BC has the lowest efficiency elasticity (-0.04) and NS the highest (-0.27). The less than one efficiency elasticities imply that an increase in efficiency does not lead to a proportional reduction in gasoline consumption by Canadian households. The difference between the actual response of gasoline demand to an increase in efficiency and the full response, where efficiency elasticity is equal to 1, is the rebound effect. Therefore, the lower the efficiency elasticity, the higher the rebound effect. The results show that the average rebound effect in Canada is 88 percent, which means that a 10 percent increase in efficiency will decrease gasoline consumption by only 1.2 percent. In other words, 88 percent of the effect of improvement in fuel efficiency is lost by increased driving or a switchto larger cars. The rebound effect varies with income group and across provinces. Specifically, it rises with income from 75 percent in low income families to 93 percent in high income families. The provincial rebound effects range from 73 percent in NS to 96 percent in BC. These results are consistent with the income group results as lower income provinces have lower rebound effect. The results of the QUAIDS model, presented in Table 6, are almost the same for income elasticities, but smaller for price elasticities and greater for efficiency elasticities by 7 percentage points on average. [Tables 5 & 6 here]

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We also estimate elasticities and implied rebound effects for the three income groups within provinces. As Table 7 shows, the elasticities vary across income groups within provinces and among provinces. The income elasticities decrease and price elasticities increase with income. The estimated rebound effects present the same pattern as before; they are smaller for high income households and provinces. Furthermore, the variation of the rebound effect in income groups is higher within the lower income provinces than higher income provinces. [Table 7 here] Since gasoline prices rose significantly along with the oil prices in the early 2000s, we further examine the effect of changes in gasoline prices on the rebound effect by re-estimating the model for the pre and post 2001 periods. The results presented in Table 8 show that income elasticities remain almost the same on average, but have increased for low-income households and decreased for the mid- and high-income households. The results also show major differences in price and efficiency elasticities before and after a hike in gasoline prices. The rebound effects are markedly greater in the high-price regime comparted to those in the low-price regime in Canada and across income levels and provinces. The exceptions are the two oil producing provinces (SK and AB), where the changes in the rebound effects in the two price regimes are small and nil, respectively. [Table 8 here] 6. Discussion Our estimations of rebound effect are higher than those reported by Small and Van Dender (2007) and Barla et al. (2009). Possible explanations for these differences are that those studies use aggregate data from the Vehicle Surveys whereas we use micro data from the Household

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Spending Survey, our models and methods of estimation are different, and we use a longer and more up-to-date data set. As Goodwin (2004) shows, the price elasticities and the rebound effects are higher when disaggregated data are used and when gasoline prices are higher, both of which apply to our data. Our estimated price elasticities of demand for gasoline and the implied rebound effects are closer to the estimates of long run elasticities using micro data. For instance, the price elasticities of gasoline demand in Canada reported by Eltony (1993) and Yatchew and No (2001) are -1, and -0.9, respectively. The uncompensated price elasticity represents the direct rebound effect, which includes substitution (price) and income effects as presented by equation (10). As Table (9) shows, the uncompensated price elasticity of gasoline is -0.88, which is divided into compensated price elasticity of -0.86 and income effect of -0.01. Since gasoline income elasticity is positive, the income effect of price change is negative, indicating the rebound effect. Furthermore, cross-price elasticity of gasoline with respect to other energy goods is negative, indicating that gasoline and other energy goods are complementary goods. That is, an increase in fuel efficiency increases not only gasoline consumption, but also other energy goods. The complementarity of energy goods might be due to a combination of technical factors and income effects; however, the latter will be more relevant in the case of gasoline and other energy goods. The cross-price elasticity of gasoline with respect to non-energy goods is positive indicating that they are substitute goods. That is, fuel efficiency leads to a decline in demand for non-energy goods. This might be explained by the fact that an increase in fuel efficiency encourages spending on new cars or appliances and, therefore, given income, consumption of non-durable non-energy goods may decrease. [Table 9 here]

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There are not many studies analysing the variations of rebound effect across income groups and economic activities, but our results seem to be in contrast with the view that the rebound effect should rise with income, as a higher income makes people less sensitive to fuel costs (Small and van Dender, 2007). Our findings show that the variations of rebound effect across income groups depends on the income level of the provinces. In other words, not all high income households are the same. In general, high income households in low income provinces are more sensitive to fuel costs and, therefore, rebound more than low-income households in those provinces. The rebound effect is also higher in provinces with larger population, which may be due to the higher traffic congestion in the large cities.

7. Conclusion In this paper, we estimate gasoline demand and rebound effect in Canada using AIDS and QUAIDS models and data from Canadian Survey of Household Spending from 1997 to 2009. The results show a rather high rebound effect in the passenger car transport and heterogeneous impacts of fuel efficiency on gasoline consumption across income groups and provinces. The rebound effect is on average between 82 to 88 percent, which suggests that doubling vehicle efficiency in Canada will lead to only 12 to 18 percent decrease in gasoline consumption. The effect decreases with income in both income group and provincial estimations. Specifically, the rebound effects for gasoline vary from 75 percent for low-income households to 93 percent for high income households. That is, high income households on average reduce their gasoline consumption less relative to low income households as cars become more fuel efficient.

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Canada has one of the highest gasoline consumption and GHG emission per capita in the world. Given the fact that passenger transport is one of the major contributors to the Canadian GHG emission, reducing gasoline consumption should be given a top priority in the environmental policies. Fuel efficiency regulations and more stringent standards for light-duty vehicles will help reduce gas consumption; however, they are not applicable to the existing vehicles and their full potential impacts will not be realized due to high rebound effects in Canada. Alternative ways to reduce fuel consumption and GHG emission further are promoting public transportation and increasing gasoline taxes, which are relatively low in Canada. Higher taxes for gasoline will make driving passenger cars more expensive even with more fuel efficient cars and will likely mitigate the rebound effect. The gasoline taxes can also be used to fund investments in infrastructures required for public transport. Our rebound effect estimates are at the higher end of the range reported in the literature. As noted earlier, the rebound effects estimated from the aggregate data are generally lower than those estimated from the micro data. One caveat in our results arises from the assumption that fuel efficiency is exogenous, whereas the choice of fuel efficient cars may depend on driving long distances or traffic congestion. The fuel efficiency may also be correlated with other vehicle attributes, such as power and reliability that affect a consumer’s utility from driving. As Frondel et al. (2008) point out, the endogeneity problems relating to fuel efficiency are more relevant to models estimating distance traveled and would not affect our estimates, which are based on fuel prices. Furthermore, as Mannering (1987) and Klier and Linn (2012) show, failing to control for endogeneity will generate a downward bias of the rebound effect. Our study also does not discern between energy prices and energy service prices. If energy prices are significantly lower than prices for energy services, the estimated rebound effect using

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energy prices will be biased downward. As Kratena and Wüger (2010) show, although energy prices have been lower than the prices of energy services in the U.S. from 1972 to 1992, they have been almost the same since then. Therefore, our estimation of rebound effect using energy prices is not likely affected by this assumption. Finally, our findings on changes in rebound effect across income groups seem to be in contrast with the expectations that high-income groups rebound less as they are less sensitive to price changes. Our conjecture is that population size, income level, prices, and economic structure may be the important factors driving the rebound effect. However, a more rigorous analysis is needed to shed more light on the issue.

Acknowledgment: We would like to thank participants at Department of Economics seminar, University of Saskatchewan, and the Canadian Economic Association conference held at Ryerson University, Toronto, in May 2013 for their helpful comments.

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25 Appendix A- Rebound Effect An improvements in fuel efficiency will lower fuel cost of driving a vehicle. Rebound effect measures the tendency to take back potential energy savings from efficiency improvements. Consider a representative consumer who allocates her/his budget between two goods/services: traveling by car (T) and other goods and services (Z). In Figure A1.a, the consumption bundle (T1, Z1) represents the optimal allocation given income, prices, and the utility level U1. Figure A1. The Rebound Effect A1.a Other Goods

Ū2

b Z2 a

Z1

c

Z3

Ū1

T1

T3 T2

Distance Travelled

A1.b

A1.c

E G E’

D(E, T1)

D(E’, T3)

Price

e

a

D(E’, T1)

G1

d

f G3 G2

a

f

P

d

T1

0