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Does a rebound eﬀect exist in solid waste management? - Panel data analysis of unit-based pricing - Takehiro Usui∗† Department of Economics, Soka university February 2008 Abstract Municipalities introduced the pricing-by-the-bag policy expecting the emissions reduction eﬀect, substitution eﬀect of recycling, and ﬁnancial beneﬁts. However, several years after the introduction of this policy, some municipalities reported an increase in waste emissions. We clarify whether or not the rebound eﬀect is statis- tically signiﬁcant, using panel data with Japanese cities’ waste emission data under volume-based pricing. We ﬁnd that the rebound eﬀect canceled out the reduction eﬀect of the pricing-by-the-bag policy approximately 19.8 years after its introduc- tion. JEL Classiﬁcation: D12, C13 Keywords: Pricing by the bag, Waste reduction, Rebound eﬀect, Panel data analysis ∗ 1-236, Tangi-cho Hachioji, Tokyo 192-8577, Japan. † Tel: +81(42)691-4016; E-mail address: usui@soka.ac.jp 1 1 Introduction In Japan, the Ministry of Environment (MOE) had encouraged and supported the intro- duction of the pricing-by-the-bag policy. Municipalities introduced this policy expecting to achieve the emission reduction eﬀect, the substitution eﬀect of recycling, and ﬁnancial beneﬁts. However, several years after the introduction of this policy, some municipalities reported an increase in waste emissions, compared to the ﬁrst year of introducing it. This phenomenon is referred to as the “rebound eﬀect.” The rebound eﬀect is considered to comprise certain composite eﬀects, namely, the over-compression of the garbage bag when people dispose waste and the announcement eﬀect that arises when the city administration advertises the signiﬁcant reduction in waste emission that occurs in the ﬁrst year since the policy’s introduction. However, this eﬀect was considered to be decreasing every year. Moreover, previous works have not clariﬁed this tentative theory. Since these works employed cross-sectional data, it was diﬃcult to detect the rebound eﬀect. On the other hand, the rebound eﬀect of pricing is relative to the time series eﬀect, however, the application of only a time-series analysis may not be useful for detecting the rebound eﬀect because of the diﬀerences in the waste policies of various cities. The use of panel data analysis will be appropriate to detect the rebound eﬀect because this estimation method combines both the cross-sectional and time-series analyses. Lin- derhof et al. (2001) employed the panel data analysis. However, they have not clariﬁed the number of years that have passed since the introduction of the pricing policy. We assume that the rebound eﬀect of the pricing-by-the-bag policy involves the follow- ing: (1) The announcement eﬀect, which is believed to be the result of the ﬁrst impression made by intensive advertisement and persuasion by public bureaucrats; and (2) Seattle stomping, a well-known phenomenon wherein people over-pack waste in the garbage bag by stomping, thereby increasing the weight per bag (Fullerton and Kinnaman, 1995). These concepts are illustrated in Figure 1. If the trend of waste generation is increasing, the decreasing eﬀect of waste generation owing to the pricing-by-the-bag policy exhibits a downward shift in the upward-sloping curve, from left to right. If a rebound eﬀect is observed, it could be represented by a large slope drawn as a continuous line or, as in 2 the alternative case the pricing-by-the-bag policy is not introduced, as a pathway on the dashed line. Finally, the slope of the continuous line meets the pathway when the pricing policy is not introduced, which implies that the rebound eﬀect will overcome the eﬀect of waste reduction. It is diﬃcult to detect the rebound eﬀect because of the need to set the control variables which means other relevant factors being equal; otherwise, it would merely identify the increasing trend of waste generation. Econometrically, almost all previous works use cross-sectional data, which is made available at certain intervals; however, there is no dynamic information on the amount of time taken by the municipality to introduce the pricing-by-the-bag policy. Then, we investigate whether or not the rebound eﬀect is statistically signiﬁcant, using Japanese cities’ waste emission data obtained through a panel data analysis under volume- based pricing. It is also useful to clarify the problem faced in designing the unit-based pricing program. The paper is organized as follows. The next section provides information on the relevant literature. Section 3 describes the econometric model employed and the type of data used. Section 4 presents a detailed report of the model selection procedure in the panel data analysis and the estimation results, and the ﬁnal section contains the concluding remarks. 2 The relevant literature There is a considerable amount of empirical literature on waste reduction; notably, there has been an increase in that on the practice of separating recyclables from waste, which has provided an incentive to unit-based pricing (Hong and Adams, 1993; Saltzman et al., 1993; Reschovsky and Stone, 1994; Callan and Thomas, 1997; Nestor and Podolsky, 1998; Hong and Adams, 1999; Sterner and Bartelings, 1999; Kinnaman and Fullerton, 2000; Jenkins et al., 2003; Callan and Thomas, 2006; Usui, 2008)1 . For example, Reschovsky and Stone (1994) examined the recycling probabilities of households using a sample of micro-data for recyclable materials. Kinnaman and Fullerton (2000) estimated the de- 1 Miranda et al. (1996) provide a survey of the unit-based pricing literature. 3 mand equations for solid waste and recycling in order to make the unit-price and curbside recycling endogenous. Jenkins et al. (2003) investigated the recycling behavior of house- holds using an ordered probit model with aggregated data on recyclable material. There are few works that have examined the eﬀect of the time that has elapsed since the introduction of the unit-price. Studies in energy economics have recently discussed the idea of a “rebound eﬀect” that is “usually assumed to have increased the search for new energy-saving technologies where eventual gains in energy eﬃciencies will reduce the real per unit price of energy services, and hence, the consumption of energy will rise and partially oﬀset the initial reduction in the usage of energy sources.”2 Bentzen (2004), Hertwich (2005), and Mizobuchi (2007) detected and discussed this eﬀect. The rebound eﬀect of the pricing-by-the-bag policy is not considered to fall under the ﬁeld of energy economics in terms of the change in energy prices owing to the improvement in eﬃciency. Though it is not equivalent and directly comparison with energy situation, gradually decreasing reduction eﬀect of this pricing policy owing to the citizens’ growing awareness of waste reduction technology like the over-compression of the bag to save using the pricing bag. Only one work by Linderhof et al. (2001) applied the panel data analysis for a dy- namic analysis. They clariﬁed the short- and long-term price elasticities of household compostable waste service demand. The eﬀect of the long-term price elasticity was found to be 30% more elastic than its short-term counterpart; moreover, they concluded that the long-term eﬀect of weight-based pricing in compostable waste would be sustainable in the future. They explain that the introduction of weight-based pricing and the exten- sive public debate that preceded it temporarily boosted people’s environmental awareness. However, this eﬀect was observed under the condition of weight-based pricing, which is not the general method followed in the pricing-by-the-bag policy. It constitutes weight-based pricing and not volume-based pricing and does not inform people regarding the disincen- tives of stomping and compressing the bag in order to accommodate additional waste in the bag. The same weight is considered regardless of whether or not people compress the bag. Fullerton and Kinnaman (1996) reported the comparison between volume-based and weight-based pricing methods. Further, they noted that the price elasticity of weight- 2 Referenced from Bentzen (2004) pp.123. 4 based pricing is more inelastic than volume-based pricing, because volume-based pricing encourages the compression and stomping of the bag. It is likely that the possibility of stomping and packing greater volumes of waste than the capacity of the bag serves as a disincentive to people. In the next section, we will explain the estimation method for the detection of the rebound eﬀect in the pricing-by-the-bag method. 3 Econometric method 3.1 Panel data analysis A panel data analysis is superior to that using cross-sectional or time-series data. This is because with cross-sectional data, it is not possible to determine when the policy was introduced, whereas with time-series data, it is not possible to ascertain the diﬀerence in eﬀect of the policy across a number of cities. The panel data analysis has considerable advantages over the cross-sectional and time series data analysis; it would be better to test the method of model selection for tournaments among the following three models: pooled OLS, the ﬁxed eﬀect model, and the random eﬀect model. The selection of an incorrect model would result in the estimated coeﬃcient not being consistent. Therefore, we introduce the F test, Breusch-Pagan test (BP test), and the Hausman test for model selection. 1) To choose between the pooling OLS and the ﬁxed eﬀect model, we apply the F test in order to determine whether or not the constant term of each observation is same in the equation. In this case, H0 implies all αi = 0, i = 1, · · · , N where i is sample number, αi is time-constant unobserved eﬀect, and Ha implies all αi ̸= 0; moreover, we employ the F test statistics. 2) To choose between the pooling OLS and the random eﬀect model, we apply the BP test to determine whether or not the variance of the constant term is signiﬁcantly diﬀerent from zero. In this case, H0 implies Var(αi ) = 0 and Ha implies Var(αi ) ̸= 0; moreover, we employ the χ2 test statistics. 3) To choose between the (1) ﬁxed eﬀect model and the random eﬀect model, we apply the Hausman test to determine whether or not the random eﬀect estimation method is biased. In this case, H0 implies that the random eﬀect is not correlated with the explanatory variables, and Ha implies that the random eﬀect is correlated with them. We choose the ﬁxed eﬀect model, if H0 5 is rejected by the χ2 statistic, where k represents the number of variables except for the (k) time dummies and the constant term. This is demonstrated through the regression model of a panel data analysis. First, to elaborate on the variables, we divide them into two parts: the ﬁrst part comprises policy variables such as the price of the bag in the various cities and the number of years that elapsed since the introduction of the unit price, and the second part comprises the socioeconomic variables. An equation is as follows: Wasteit = β1 P riceit + β2 Y earselapsedit × P riceit + β3 Y earselapsed2 × P riceit it 2 +β4 P opdit + β5 P opd2 + β6 Incomeit + β7 F amilyit + β8 F amilyit it ∑ +β9 Ageit 65 + γt Yeart + αi + uit (1) t 3 Price is deﬁned as the price per bag or tag (40-50 liter bag/yen). We choose the following independent variables: Yearselapsed, representing the number of years that have passed since the introduction of the unit-price, which is important to detect the rebound eﬀect because it is believed to be dependent on the years that have elapsed since the introduction of the pricing; Popd and Popd2 , representing population density (1000 person/km2 ) and its squared value which may aﬀect to illegally dispose waste, because the rural (low-density) areas as well as the urban (high-density) is thought of as dumping easily; Income, representing taxable gain per capita (million yen); Family and Family2 , representing the average household size and its squared value which may take positive. This may also include mixed eﬀects. The ﬁrst is the eﬀect of decreasing consumption. A large household size will increase household consumption but decrease per capita consumption. The second is the eﬀect of shared housework which occurs for large households. We also want to see the turning point.; Age 65, representing the percentage 3 However, this price is perceived as being endogenous. Previous works such as Kinnaman and Fullerton (2000) and Callan and Thomas (2006) have tested its endogeneity. This paper also tests the endogeneity of this variable. The instrumental variable is Pop, which represents the population of the city; this variable may aﬀect the timing of the introduction of the policy and the magnitude of its price. We now proceed to testing the instrumental panel data and the normal panel data models. The endogeneity test should be conducted by applying the well-known Hausman’s endogeneity test. We apply this test (Hausman, 1978) to each model, namely, the exogenous pooled OLS, ﬁxed eﬀect, and the random eﬀect models. Since all the chi-squared statistics are not signiﬁcant, we do not reject the null hypothesis and refrain from using the IV panel estimator of our model 6 of people aged over 65 which may be due to diﬀerences in consumption habits within this age group, and; Yeart , representing the time dummy variable, which is 1 if the current sample is in the reference year (t), and 0 otherwise. 3.2 Data Solid waste comprises waste generated in households and business establishments4 . Al- though they should typically be examined separately, in certain municipalities, they are not always segregated from the other waste. There is an advantage to using joint data on both solid and other wastes in terms of the fact that the waste pricing may lead citizens to illegally transfer their household waste to business waste dumps. Therefore, combining the waste data will help in maintaining consistency. This study also deﬁnes waste as the sum of the amount of combustible waste, noncombustible waste, recyclable waste, and recyclables collected by citizens. In addition, the municipal waste data has been derived from the Japan Waste Management Association (1995-2002).5 Panel data pertaining to the waste emissions of each municipality spanning an eight- year period from the 1995 to 2002 ﬁscal years were provided by the Japan Waste Man- agement Association (1995-2002). Although waste pricing data is not published by the MOE, Yamaya (2006) gathered waste pricing data and the years that have elapsed since the introduction of the pricing for all Japanese cities using questionnaires administered through mail and telephone. The data collected pertained to the price of garbage bag for 40-50 liter (approximately 12 gallons) of waste per garbage bag or the corresponding tag used to levy a fee on the waste generated in 712 cities and 23 wards of Tokyo in February 2005. Figure 2 presents the histogram of this pricing data. Histogram shows that mode is 40 yen per bag. Note that waste pricing data is priced only for combustible waste; further, the data does not include pricing for recyclable waste. Other socioeconomic data with respect to all municipalities was obtained from the Asahi (2003). This data includes information on the taxable gain per capita, the average household size, number of residents over the age of 65, and population density. 4 In Japan, municipalities collect solid waste called as general waste, which basically comprises both household solid waste and waste generated from business activities. 5 The amount of waste does not include the following: (1) “the amount of self disposal” and (2) “the amount of waste carried into municipal facilities.” 7 Finally, we adopt the data for the eight-year period on 665 cities of the 712 cities, taking into consideration the municipal mergers during this period and making the relevant exclusions. The descriptive statistics and the deﬁnitions of the variables are presented in Table 1. 4 Estimation result We discuss the estimation results; however, we consider only the ﬁxed eﬀect estimates, since we apply the model selection method discussed in subsection 3.16 . We show the Table of the estimation results (Table 2). Model 1, 2, 3, and 4 are the estimated results of ﬁxed eﬀect model. Model 1 and 2 exclude Yearselapsed×Price and its squared. Model 3 and 4 include them. Time dummy are included in model 1 and 3. The number of years that elapsed since the introduction of the unit price are eﬀective in terms of model ﬁtting because AIC in the model 1 and 3 are smaller than AIC in the model 2 and 4. The coeﬃcient of the Price whose models’ Yearselapsed×Price and its squared are excluded is over estimated, but the diﬀerence isn’t so large. Also including the time dummy, Year 1996 - Year 2002 is made improvement of the model ﬁtting, since controlling for the unobservable eﬀects. Eventually, Model 3 is most desirable one. We explain only Model 3 in next subsection. 4.1 Pricing by bag The pricing-by-the-bag method comprises three terms, β1 , β2 , and β3 . The marginal eﬀect of the price depends on Yearselapsed and Yearselapsed2 . Prior to calculating the marginal eﬀect and its elasticity, we test the signiﬁcance of the joint restriction. We are primarily interested in the rebound eﬀect. To test this eﬀect, we employ the F test on the joint restrictions of the coeﬃcients as follows: H0 : (β1 , β2 , β3 ) = (0, 0, 0), H1 : (β1 , β2 , β3 ) ̸= (0, 0, 0) (2) 6 First, we apply the F test of the pooled OLS model vs. the ﬁxed eﬀect model. As a result, we reject the null hypothesis and select the ﬁxed eﬀect model. Next, we apply the BP test of pooled OLS vs. the random eﬀect. Based on the test results, we reject the null hypothesis and select the random eﬀect model. Finally, we employ the Hausman test of ﬁxed eﬀect vs. random eﬀect. The result shows that we should reject the null hypothesis and employ the ﬁxed eﬀect model. 8 If the null hypothesis is rejected and the sign of the β3 which is the coeﬃcient of the squared Yearselapsed, is statistically signiﬁcant and the coeﬃcient of the quadratic term is positive whose the shape of the quadratic curve is convex in the downward direction, the existence of the rebound eﬀect is conﬁrmed7 : F (3, 4626) = 9.58∗∗∗ . Then, the null hypothesis is rejected, and the β3 is highly signiﬁcant and positive. Thus, we ﬁnd that the rebound eﬀect exists. If we diﬀerentiate equation (1) by P rice of equation (1) and set it as zero, then ∂Waste = −1.73 − 0.00447Y earselapsed + 0.0067Y earselapsed2 (3) ∂P rice We solve the quadratic equation relative to Yearselapsed, Yearselapsed= 19.8 which passed the F test. This implies that approximately 19.8 years after the introduction of the pricing-by-the-bag policy, the rebound eﬀect cancels out the reduction eﬀect arising from pricing by the bag. The ﬁrst and second eﬀects have been discussed previously; they cancel out the reduction eﬀect of the pricing; however, the third eﬀect is considered to enhance the waste reduction eﬀect with each passing year. Summing up these eﬀects, the net eﬀect will be positive. We interpolate the estimated value of β1 , β2 and β3 from equation (1) and derive the shape of the rebound eﬀect, as illustrated in Figure 3; the value of this eﬀect is predicted to be the same as the sample mean of the Price controlled by other variables. Then, we value Yearselapsed at the sample mean of the introduction of the unit-price and derive the price elasticity of the waste emission, as follows: ∂Waste = −1.73 − 0.0447 · (9.58) + 0.0067 · (9.58)2 ∂P rice = −0.939 ( ) ∂Waste P rice 35.75 · = −0.939 · ∂P rice Waste 853.0 ϵprice = −0.039 where Price and Waste is sample mean of the unit-price cities. The estimated Price elasticity, evaluated at the point of mean is −0.039. If we estimate the elasticity from the ( ) ( ) 7 RSSR − RSSUR RSSUR That is, a joint F test of the three restricts, F0 = / , where RSSR is p n−k the restricted sum of the squared residual; RSSUR is the unrestricted sum of the squared residual; p is the number of restrictions; and n − k denotes the degrees of freedom. 9 Model 1 which is excluded the variable of Yearselapsed, Price elasticity is −0, 076 which is over estimated. Our estimated value of the magnitude of price elasticity is smaller than −0.226 in the case of volume-based pricing (Fullerton and Kinnaman, 1996), however this one is included of substitution eﬀect of recycling. The value of −0.25 in that of short-run elasticity (Linderhof et al., 2001) is weight-based pricing system. Consequently, the eﬀect is not included “Seattle stomping”. Eventually, our estimation result is not so small. If their estimation considered the Yearselapsed, their elasticities may be partially reduced due to the rebound eﬀect. 4.2 Socio-economic variables The values of population density, Popd and Popd2 , are determined depending on whether or not the community tends to illegally dispose waste, because the rural (low-density) areas as well as the urban (high-density) areas tend to dump easily. Although the t test does not produce signiﬁcant values for these variables, the joint F test is signiﬁcant and rejects the hypothesis wherein both coeﬃcients are zero. We calculate the turning ∂Waste point using the variable = −36.21 − 3.52P opd = 0, P opd = −10.29. It appears ∂P opd that the greater the increase in the population density, the more is the decrease in waste emission, and there is no limit to this trend. Although this contrasts with the suggestions of Fullerton and Kinnaman (1996) and Callan and Thomas (2006), it should be noted that it is not easy to uncover illegal dumping activities in rural areas as compared to the case in urban areas. The taxable gain per capita, Income, is a proxy variable for average per capita income and represents the opportunity cost of time. The coeﬃcient is signiﬁcant and positive for the ﬁxed eﬀect model. This income elasticity of waste demand is 0.29, which is evaluated at the sample mean of waste (962.1) and income (1.37). Other works have estimated the income elasticity, for example, at 0.26 (Kinnaman and Fullerton, 2000); however, the elasticity in these cases captures the joint estimation of recycling. The average household sizes in each municipality, Family and Family2 are both highly signiﬁcant. Diﬀerenced by Family, we derive the turning value as 2.59, which is a concave functional form. The greater the increase in the household size until 2.59, the greater will be the waste generation; however, beyond 2.59, the waste generation will decrease. 10 This result is diﬀerent from that presented by Callan and Thomas (2006). In terms of the lifestyle, collective consumption aﬀects the waste reduction, for example, each household subscribes to only one newspaper, whereas it is likely that a single-person household does not subscribe to even a single a newspaper. The percentage of people aged over 65, Age 65, is signiﬁcant and negative. A 1% point increase of Age 65 reduces waste generation by 11.8 g per person per day. In the study conducted by Kinnaman and Fullerton (2000), it was 24.9 g (this has been converted from pounds per year to grams per day). Apparently, this results from the fact that elderly people appear to have considerable time and can therefore reduce waste generation. 5 Conclusion We apply the test of model ﬁt among the three panel data models, based on which the ﬁxed eﬀect model is selected. The estimation result reveals that the coeﬃcient of price is statistically signiﬁcant and its marginal eﬀect on the price is dependent on Yearselapsed and Yearselapsed2 ; further, the value of the elasticity is −0.039 at the sample mean of Yearselapsed. This magnitude is smaller than −0.226 of volume-based pricing (Fullerton and Kinnaman, 1996) and −0.25 of short-run elasticity (Linderhof et al., 2001). We also test the rebound eﬀect and ﬁnd that it does exist; moreover, we ﬁnd that the shape of the function is quadratic, and therefore, the greater the number of years that have elapsed, the larger is the rebound eﬀect. Our study is, however, not without some limitations. We were unable to determine the eﬀect relative to the recycling of waste. It is important to ascertain this eﬀect and to consider segregating recyclables and non-recyclables by using a multi-equation model approach and applying a three-stage least squares estimation in the framework of a panel data analysis. References [1] Asahi Shimbun, 2003. Minryoku 2002 CD-ROM. (In Japanese). 11 [2] Bentzen, J., 2004. Estimating the Rebound Eﬀect in US Manufacturing Energy Con- sumption. Energy Economics, 26, 123-134. [3] Callan, S.J., Thomas, J.M., 1997. The Impact of State and Local Policies on the Recycling Eﬀort. Eastern Economic Journal, 23 (4), 411-423. [4] Callan, S.J., Thomas, J.M., 2006. Analyzing Demand for Disposal and Recycling Services: A Systems Approach. Eastern Economic Journal, 32 (2), 221-240. [5] Fullerton, D., Kinnaman, T.C., 1996. Household Responses to Pricing Garbage by the Bag. American Economic Review, 86, 971-984. [6] Hertwich, E.G., 2005. Consumption and the Rebound Eﬀect: An Industrial Ecology Perspective. Journal of Industrial Ecology, 9 (1-2), 85-98. [7] Hong, S., Adams, R.M., 1993. An Economic Analysis of Household Recycling of Solid Wastes: The Case of Portland, Oregon. Journal of Environmental Economics and Management, 25 (2), 136-46. [8] Hong, S., Adams, R.M., 1999. Household Responses to Price Incentives for Recycling: Some Further Evidence. Land Economics, 75 (4), 505-514. [9] Japan Waste Management Association, 1995-2002. Haikibutsu syori jigyo jittai chosa toukei siryou (general waste). Results for 1995-2002. (In Japanese). [10] Jenkins, R.R., Martinez, S.A., Palmer, K., Podolsky, M.J., 2003. The Determinants of Household Recycling: A Material-Speciﬁc Analysis of Recycling Program Features and Unit Pricing. Journal of Environmental Economics and Management, 45 (2), 294-319. [11] Hausman, J. A., 1978. Speciﬁcation Tests in Econometrics. Econometrica, 46 (6), 1251-1271. [12] Kinnaman, T.C., Fullerton, D., 2000. Garbage and Recycling with Endogenous Local Policy. Journal of Urban Economics, 48 (3), 419-442. 12 [13] Linderhof, V., Kooreman, P., Allers, M., Wiersma, D., 2001. Weight-based Pricing in the Collection of Household Waste: The Oostzaan Case. Resource and Energy Economics, 23, 359-371. [14] Miranda, M.L., Bauer, S.D., Aldy, J.E., 1996. Unit Pricing Programs for Residential Municipal Solid Waste: An Assessment of the Literature. U.S. EPA. [15] Mizobuchi, K., 2007, Estimation of the Rebound Eﬀects caused by an Improvement in Energy Eﬃciency: Japanese Household Consumption and Emission, Environmental Science, 20 (1), 61-70 [16] Nestor, D.V., Podolsky, M.J., 1998. Assessing Incentive Based Environmental Policies for Reducing Household Waste Disposal. Contemporary Economic Policy, 16 (4), 401- 411. [17] Reschovsky, J.D., Stone, S.E., 1994. Market Incentives to Encourage Household Waste Recycling: Paying for What You Throw Away. Journal of Policy Analysis and Management, 13 (1), 120-139. [18] Saltzman, C., Duggal, V.G., Williams, M.L., 1993. Income and the Recycling Eﬀort: A Maximization Problem. Energy Economics, 15 (1), 33-38. [19] Sterner, T., Bartelings, H., 1999. Household Waste Management in a Swedish Munic- ipality: Determinants of Waste Disposal, Recycling and Composting. Environmental and Resource Economics, 13 (4), 473-491. [20] Usui, T., 2008. Estimating Eﬀect of Unit-Based Pricing under the Japanese Recycling Law; In the Presence of Sample Selection Bias, Ecological Economics, 66, 282-288. [21] Yamaya, S., 2006. Katei-gomi Yuryoka no Genjo to Kadai, Life and Environment, 51(1), 13-20. (In Japanese) 13 waste emission Rebound effect Waste Reduction by pricing the bag t Figure 1: Rebound eﬀect .025 .02 .015 Density .01 .005 0 0 50 100 150 PRICE Figure 2: Histogram of price 14 Table 1: Descriptive statistics Variable Mean S.D. Deﬁnition N = Waste 962.1 202.6 Volume of solid waste collection (in gram per capita per day) 5314 Waste 853.0 187.5 Volume of solid waste collection (in gram per capita per day) 823a) Price 35.75 16.42 Price per bag or tag (yen/40-50 liter bag) 824 Yearselapsed 9.580 10.31 Number of years since the introduction of the unit price 824 Popd 1.704 2.314 Population density (1000 person /km2 ) 5313 Income 1.370 0.304 Taxable gain per capita(million yen) 5320 Family 2.860 0.343 Average household size (number per household) 5313 Age 65 0.171 0.171 Percentage of people aged over 65 (% over 65 years of age) 5320 a) Number of observations with introducing pricing by bag. Table 2: Estimation results Model 1 Model 2 Model 3 Model 4 Price -1.81 -1.75 -1.73 -1.71 (0.1) (0.1) (0.1) (0.1) Yearselapsed×Price -0.0447 -0.0122 (0.046) (0.0464) Yearselapsed2 ×Price 0.0067 0.0072 (0.002) (0.002) Popd -36.59 63.62 -36.21 63.08 (39.7) (38.6) (39.7) (38.6) Popd2 -1.82 -5.34 -1.76 -5.21 (2.9) (3.0) (2.9) (3.0) Income 217.49 75.83 206.12 74.08 (29.6) (17.3) (29.9) (17.3) Family 712.32 541.14 685.84 522.57 (106.9) (104.3) (107.1) (104.1) Family2 -137.14 -147.66 -132.41 -142.25 (16.6) (16.5) (16.6) (16.5) Age 65 -1,195.78 914.16 -1,183.96 887.08 (270.9) (184) (270.5) (183.7) Constant -21.75 324.14 24.47 333.03 (217.4) (196.8) (217.7) (196.3) Observations 5,307 5,307 5,307 5,307 Year controls? Yes No Yes No Adjusted R2 0.247 0.222 0.250 0.226 AIC 59242.2 59399.3 59227.1 59375.9 Note : Standard errors in parentheses. 15 16 Figure 3: Prediction of the rebound eﬀect despalesraeY 08- 06- Was et 04- e mi s s 02- o 52 02 51 01 5 0 n(i g ar 0 m p/ 02 er s o n 04 da y) 06

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