Waste Management xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Waste Management journal homepage: www.elsevier.com/locate/wasman
Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China Hui-zhen Fu, Zhen-shan Li ⇑, Rong-hua Wang Department of Environmental Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
a r t i c l e
i n f o
Article history: Received 19 November 2014 Accepted 17 March 2015 Available online xxxx Keywords: Estimate Waste generation Activity Resident group
a b s t r a c t The quantities and composition of municipal solid waste (MSW) are important factors in the planning and management of MSW. Daily human activities were classified into three groups: maintenance activities (meeting the basic needs of food, housing and personal care, MA); subsistence activities (providing the financial support requirements, SA); and leisure activities (social and recreational pursuits, LA). A model, based on the interrelationships of expenditure on consumer goods, time distribution, daily activities, residents groups, and waste generation, was employed to estimate MSW generation by different activities and resident groups in five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) of China. These five provinces were chosen for this study and the distribution patterns of MSW generated by different activities and resident groups were revealed. The results show that waste generation in SA and LA fluctuated slightly from 2003 to 2008. For general waste generation in the five provinces, MA accounts for more than 70% of total MSW, SA approximately 10%, and LA between 10% and 16% by urban residents in 2008. Females produced more daily MSW than males in MA. Males produced more daily MSW than females in SA and LA. The wastes produced at weekends in MA and LA were far greater than on weekdays, but less than on weekdays for SA wastes. Furthermore, one of the model parameters (the waste generation per unit of consumer expenditure) is inversely proportional to per-capita disposable income of urban residents. A significant correlation between gross domestic product (GDP) and waste generation by SA was observed with a high coefficient of determination. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Solid waste research has attracted increasing attentions in recent decades, while solid waste management was one of the hot issues based on the statistical analysis of author keywords (Fu et al., 2010). The prediction of municipal solid waste (MSW) generation plays an important role in solid waste management (Dyson and Chang, 2005). The amount and composition of waste generation provides the basic information needed for the planning, operation and optimization of waste management systems (Beigl et al., 2008). Modeling is a tool used for the planning and management of municipal waste (Benítez et al., 2008). Conventional prediction models include correlation analysis models (Dennison et al., 1996a,b), multiple regression analysis models (Hockett et al., 1995), Single regression analysis models (Thøgersen, 1996), group comparison (Parfitt et al., 2001), system dynamics (Dyson ⇑ Corresponding author at: No. 5, Yi Heyuan Road, Haidian District, Department of Environmental Engineering, Peking University, Beijing 100871, China. Tel.: +86 10 6275 3962; fax: +86 10 6275 6526. E-mail address: [email protected] (Z.-s. Li).
and Chang, 2005), time series analysis (Leao et al., 2001), gray fuzzy dynamic models (Chang and Lin, 1997; Chen and Chang, 2000), and input–output analysis models (Gay et al., 1993; Patel et al., 1998; Joosten et al., 2000). Estimation models play an important role in predicting the MSW amount and composition, but most of these models are incapable of describing the waste generation if the waste is generated by different activities and is from different resident groups. Modeling the waste generation distribution is a useful method to anticipate the design of waste management strategies as a function of consumption and demographic development, and to plan for future management needs (Purcell and Magette, 2009). Researchers examined waste generation from many different angles, especially in recent years. They explored rapid methods for estimating the rate of solid waste generation (Ibiebele, 1986), identified the waste spatial distribution (Purcell and Magette, 2009; Yenice et al., 2011), waste composition distribution (Moriwaki et al., 2009), and time-related waste distribution (Boldrin and Christensen, 2010), to aid more targeted waste planning and policy decisions.
http://dx.doi.org/10.1016/j.wasman.2015.03.029 0956-053X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
2
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
As a whole, when compared with existing predictions or estimates for MSW generation, the estimation of MSW distribution by different activities and resident groups at a city level, in this paper presents a novel and reliable view for the analysis of MSW generation. Moreover, it is difficult to obtain the amount of waste generated by different activities and resident groups by field surveys, because it is hard to measure waste generation by different resident groups in different time. Recently, a new model based on the relationship of consumption and waste was recently proposed to describe the MSW generation in different activities and by different resident groups (Li et al., 2011). This study employed this model to present waste generation by different resident groups in different time for a wider area of five provinces in China. Five provinces which have top economic development levels and different geographical positions were chosen to study the differences in waste generation for each activity of male and female residents on weekday and weekend. This will help the development of waste management policies and understand MSW generation patterns beyond the current prediction techniques. Furthermore, a high correlation coefficient was observed between the parameters of the newly developed model and economic related indicator. 2. Model Waste generation has long been a consequence of human activity (Louis, 2004). It is the result of a production and consumption cycle which starts with the production of consumer goods and continues with the generation, collection, storage, and final disposal of MSW. All manufactured, commercialized and consumed products are finally converted, or at least partially converted, into waste (Benítez et al., 2008). A new model based on the relationship of consumption and waste was proposed to describe the MSW generation in different activities and by different resident groups (Li et al., 2011). The conceptual model and mathematical model were presented sequentially. 2.1. Conceptual model The essential reason for MSW generation is the consumption of consumer goods in human beings’ daily activities. Consumption requires time, and human activities consume goods (Jara-Diaz, 2003). Consumer goods eventually turn into disposal in part (Benítez et al., 2008). People play an important role as the users who utilized consumer goods and the producers of waste. Therefore, the concrete relationships between residents and MSW generation in a city can be divided into four levels: resident groups, consumer goods, activity and waste composition, as shown in Fig. 1 (Li et al., 2011). Each level fell into groups, and the line relationships turn to complex distributions between adjacent levels. All the people in the city were divided into Rn groups (R1, R2, . . ., Ri, . . ., Rn). Each group of residents conduct An groups of activities (A1, A2, . . ., Ai, . . ., An), and in each activity group, Cn groups of consumer goods (C1, C2, . . ., Ci, . . ., Cn) were consumed and each group of consumer goods finally converted to Wn groups of waste composition (W1, W2, . . .,Wi, . . ., Wn). Ultimately, the total quantity of MSW is the sum of various waste groups derived from consumer goods in different activities by all people in the city. The important relationships were obviously involved, between resident group and activities, between activities and consumer goods, and between consumer goods and waste composition. Three important parameters were therefore proposed based on these three relationships to describe the waste generation.
2.2. Mathematical model Initially, two assumptions were proposed (Li et al., 2011): (1) The research target of the model was the group behavior instead of individual behavior. Residents, consumer goods, and waste were classified into groups. Thus, the gender, age, education, and other personal features of the actors were not included in the model. (2) The quantity of waste generation was only determined by consumer expenditure. All the consumer goods consumed in one year were finally converted into MSW. The consumer goods of one group share the same rate of conversion. The quantity of waste generation is directly proportional to consumer expenditure and the waste generation per unit consumer expenditure can be considered constant in a short time (i.e. > > = < 1> k2 (1) K i ¼ ; ki , waste generation per unit of consumer >...> > > ; : ki expenditure in year i. (2) C j ¼ f c1 c2 . . . cj g; cj , consumer expenditure distribution to activity j in unit time. 9 8 > > > r11 r12 . . . r1j > = < r21 r22 . . . r2j ; r , waste generation rate of (3) Rij ¼ > ij > ... ... ... ...> > ; : ri1 ri2 . . . rij activity j in year i.
Males and females were the two residents groups analyzed here (m = 1, stands for male; m = 2, stands for female). Then, waste production by resident group m in activity j in year i can be calculated by time spent on activity j by resident group m (t mj ) and the waste generation rate of activity j in year i (r ij ) based on Eq. (3):
xmj ¼ tmj r ij where
ð3Þ
t 11 t 12 . . . t1j ; tmj , time assigned to activity j t 21 t 22 . . . t2j by resident group m. x11 x12 . . . x1j ; xmj , waste generation rate of (2) X mj ¼ x21 x22 . . . x2j activity j by resident group m in year i. (1) T mj ¼
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
3
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Resident group
R1
R2
Ri
Rn
...
...
A1 Ai An
Activity
C1 Ci
Cn
...
...
...
...
An
...
...
...
...
...
... ...
...
W1 Wi Wn W1 Wi Wn
...
...
...
Waste compostion
Cn
...
C1 Ci Cn C1 Ci
...
...
...
Consumer goods
A1 Ai An A1 Ai
...
W1 Wi Wn W1 Wi Wn
Parameter III Parameter II Parameter I
All the People in a City
Time assignment to activities by resident groups
Consumer expenditure distribution to activities
Waste generation per unit consumer expenditure
Total Municipal solid waste in a City Fig. 1. Structure of MSW generation in a city.
3. Data source and data processing 3.1. Waste generation of Study area The waste generation was related to consumption (Benítez et al., 2008), and thus the five provinces of China (Zhejiang in eastern China, Guangdong in southern China, Hebei in northern China, Henan in the Central China area, and Sichuan in Southwest China) with higher gross regional product of China were chosen for further analysis. Table 1 lists the basic demographics and economical information of these five provinces in 2008. The yuan, the monetary unit of China, was used as the unit of consumer expenditure for detailed calculations. Guangdong have the highest gross regional product of 1886.4 billion yuan in 2008 according to the National Bureau of Statistics (NBS) of China. Guangdong also hold the greatest populations, while there were the smallest populations of Zhejiang. There were more males than females in all of these five provinces. Sichuan had the smallest percentage of the persons aged 15–64 and the least gross regional product of 1250.6 billion yuan. Five provinces had different percentages of product from three strata of industry to gross domestic product (GDP). Fig. 2 shows the waste production of the urban area in the five provinces during 2003–2008. Guangdong produced the most waste in China in 2008 and in the period of 2003–3008, and the waste production kept increasing during 2003–3008. Sichuan contributed the least waste in these five provinces. This performance is consistent to its small gross regional product, which confirmed the assumption that waste generation is related to economic level. The waste production of Zhejiang, Henan and Hebei fluctuated similarly.
Fig. 2. Waste production of five provinces during 2003–2008.
3.2. Volume of retail sales of consumer goods As GDP has increased, so has the volume of waste produced, because economic prosperity affects waste generation by stimulating increased consumer activity and business expansion (Mazars, 2003). Based the newly developed model, the consumer expenditure could be used for the Parameter II consumer expenditure distribution to activities. The consumer expenditure data set for this paper was obtained from the statistical yearbook (2003–2008) for these five provinces administered by the National Bureau of Statistics. The statistical data did not consider inflation and
Table 1 Demographics and economical information of five provinces in 2008. Province
Zhejiang Guangdong Hebei Henan Sichuan
UR
2700.2 5241.3 2928.0 3573.0 3041.6
GRP
2148.7 3569.6 1618.8 1840.8 1250.6
Sex ratio
1.039 1.051 1.040 1.023 1.026
% Aged 0–14
Aged 15–64
Aged > 65
PI
SI
TI
14.0 18.5 16.1 19.8 17.4
75.3 74.0 75.2 72.3 71.2
10.7 7.56 8.74 7.82 11.4
5.1 5.5 12.6 14.4 18.9
53.9 51.6 54.2 56.9 46.3
41 42.9 33.2 28.6 34.8
UR: Urban residents (Unit: 104 persons); GRP: Gross regional product (Unit: billion yuan); Sex ratio: The sex ratio of male to female; PI: the percentage of the product in primary industry to gross domestic product; SI: the percentage of the product for secondary industry to the gross domestic product; TI: the percentage of the product in tertiary industry to gross domestic product; 1000 yuan = 160 dollars.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
4
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Table 2 Total volume of retail sales of consumer goods by urban residents in five provinces. Year
2003 2004 2005 2006 2007 2008
Zhejiang
Guangdong
Hebei
Henan
Sichuan
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
113.4 113.8 110.9 109.9 109.0 105.0
2.5 2.7 2.6 2.5 2.5 2.4
2285.6 2388.9 2493.3 2604.5 2815.4 3202.6
100 102.9 101.8 101.5 103.4 106
3.4 3.4 3.5 3.5 3.9 3.8
4934.9 5489.7 6376.8 7404.9 8490.3 9674.3
100.2 103.2 101.1 101.5 104.1 106.7
2.5 2.5 2.6 2.7 2.7 2.8
1219.7 1401.7 1730.6 2012.3 2335.6 2769
101.6 105.4 102.1 101.3 105.4 107.5
3.6 3.4 3.4 3.4 3.4 3.4
1437.1 1598 1852.1 2185.8 2531.7 2964.8
100.1 103.7 100.6 101.7 105.3 105.3
3.3 3.2 3.1 3.2 3.2 3.2
1341.0 1499.1 1729.5 2000.5 2280.6 2688.2
RPI, Retail Price Index; U/R, ratio of consumption level of urban residents to rural residents; UVRS, urban volume of retail sales (Unit: 108 yuan); 1000 yuan = 160 dollars.
covered both the rural and urban area. Therefore, some pretreatment needed to be conducted to obtain the consumer expenditure of urban area. Firstly, to eliminate the effects of the inflation rate for the total volume of retail sales of consumer goods, the Retail Price Index (RPI), which reflects the general change in retail prices of commodities was employed. Secondly, the ratio of the consumption level of urban residents to rural residents (U/R) and the population of urban and rural areas were also developed to calculate the total volume of retail sales of consumer goods in urban areas (Urban Volume of Retail Sales, UVRS). Retail price index, ratio of consumption level of urban residents to rural residents, and urban volume of retail sales are displayed in Table 2. After the correction by retail price index and deduction of rural consumer expenditure, urban volume of retail sales (UVRS) are shown in Table 2, increasing steadily from 2003 to 2008 for the five provinces. In comparison to waste generation, the consumer expenditure of Guangdong was also the most among these five provinces. The fact that Guangdong had the most population, followed distantly by other four provinces, might be one of the reasons. The least waste in Sichuan could partly explain the least volume of retail sales of consumer goods of it. 3.3. Time allocation among the three basic activities The time allocation were derived from the 2008 time-use survey designed and administered by the National Bureau of Statistics (2008), administered in China. This survey was initiated on May 2008, and was the first time survey of the activities of Chinese individuals by Chinese government. The time survey provides time use information for these five provinces (Zhejiang, Guangdong, Hebei, Henan, and Sichuan). The urban respondents recorded daily life in a diary. In addition, they were asked to record their activities during 144, 10-min time slices over the period of the day. If more than one type of activity was pursued during the time slice, all the activities were recorded by the respondent. The respondent marked the primary activity and the secondary one. Respondents recorded the activity under one of the several detailed pre-coded activity types. These pre-coded activities included personal activity; paid work; primary production activities not for establishments; building and extensions of dwelling; services for income and other production of goods not for establishments; housework; caring for and helping household or nonhousehold members; education and training, recreation and social interaction; and other activities (not classified elsewhere). The respondent recorded the time allocation information over any two days, where one is a weekday and the other is the weekend. They recorded the time distribution of activities over two days. Then the time of each equivalent activity was calculated by the averages of weight (the weekdays account for 5/7 while the weekends for 2/7). The result is the time allocated to one activity. The whole data production process was affected by the classification used, and therefore the quality of the resulting figures depends on how well this classification reflected the types of
activities carried out by the population. The activities were classified into three categories, subsistence activity, maintenance activity, and leisure activity, which were first identified by Bhat and Koppelman (1993). According to this classification, subsistence activity referred to the supply of work or work-related business, and was essential for providing the financial requirements for pursuing maintenance and leisure activities. Maintenance activity pertained to the purchase and consumption of goods/services to satisfy household/personal physiological needs and biological needs. Leisure activity referred to social and recreational pursuits motivated by cultural and psychological needs. The detailed categories of activities are shown as follows: (1) maintenance activities (MA) included the following activities: housework; caring for and helping household or non-household members; community service; public benefit activities; eating; drinking; smoking; sleeping; personal care activities; and related transport activities. (2) subsistence activities (SA) included the following activities: work; workrelated activities; family primary production activities; family manufacturing activities; construction activities; family service operations; education; training activities; and work-related transport activities. (Students were a special group in this study, because they did not have subsistence time. The students’ duties were learning, so the learning time belonged to this category in the study.) (3) leisure activities (LA) included the following activities: TV viewing; reading, listening to the radio; sport/hobbies; entertainment; and socializing. The average time of the urban individuals engaged in SA, MA, and LA in one day was calculated. The time allocated to all the activities in a single day did not total 24 h, because the sleeping time was excluded. We therefore used the percentage of time allocated to one activity to represent the time allocated to the activity. 4. Results and discussion The data investigated in Section 3 were substituted in Eq. (2), and then the three parameters of the newly developed model including Parameter I: the waste generation per unit of consumer expenditure, Parameter II: consumer expenditure distribution to activities, and Parameter III: The ratio of time spent on different activities by residents in five provinces could be obtained. 4.1. Parameter calculation 4.1.1. Parameter I: The waste generation per unit of consumer expenditure The annual waste generation divided by total volume of retail sales of consumer goods equals parameter I – waste generation per unit of consumer expenditure (ki ), in accordance with above assumption, suggesting the conversion of consumer expenditure to waste. The greater the ki , the greater the waste produced from the same consumer goods. The waste generation per unit of consumer expenditure in Zhejiang, Guangdong, Henan, Hebei and Sichuan are displayed in Fig. 3. ki displayed different downward
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Fig. 3. Waste generation per unit of consumer expenditure in five provinces (kg/ yuan).
trends of these five provinces from 2003 to 2008. In Hebei, the ki parameter decreased from 0.058 kg/yuan in 2003 to 0.024 kg/yuan in 2008, in Henan it fell from 0.045 kg/yuan to 0.026 kg/yuan and in Sichuan it fell more rapidly from 0.041 kg/yuan to 0.020 kg/yuan compared with Guangdong and Zhejiang with falls of 0.029 kg/ yuan to 0.019 kg/yuan and 0.030 kg/yuan to 0.025 kg/yuan, respectively. It is also noticed that the gap between these five provinces has reduced from 0.029 kg/yuan in 2003 to 0.007 kg/yuan in 2008. Although Guangdong produced the most waste and consumer expenditure, it has the least waste generation per unit of consumer expenditure. This might be partly attributed to the largest percentage (42.9%) of the product in tertiary industry to GDP (Table 1). The per-capita disposable income of urban residents is closely related to the retail sales of consumer goods, and therefore the waste production. The relationship linking per-capita disposable income of urban residents with waste generation per unit of consumer expenditure was capable of being expressed by the mathematical formula:
0:0028 ¼
1 K I
It was also expressed as:
K I ¼ 357 where K is waste generation per unit of consumer expenditure, I per-capita disposable income of urban residents. The product between K and I was almost constant, which indicated that the more per-capita disposable income of urban residents, the lower the waste generation per unit of consumer expenditure. It could be partly explained by the increasing importance of the inner quality of consumer goods to high income people. Based on the same consumer expenditure, the high income people would produce less waste than the low income people. That was why the reciprocal of per-capita disposable income of urban residents is proportional to waste generation per unit of consumer expenditure (Fig. 4). 4.1.2. Parameter II: Consumer expenditure distribution to activities Activity has a significant connection which links consumer goods to waste generation. The weight distribution of consumer expenditure to the three activities was calculated by consumer goods cluster. People consume various consumer goods when involved in different activities. Depending on the activity category, all consumer items from statistical books were classified
5
Fig. 4. Relationship between per-capita disposable income of urban residents and waste generation per unit of consumer expenditure.
into three categories. The consumer goods of MA contains grain, starches and tubers, oil and fats, meat, poultry and related products, eggs, aquatic products, vegetables, condiments, sugar, tobacco, liquor and beverages, dried and fresh melons and fruits, nuts and kernels, cake, milk and dairy products, other food, garments, clothing materials, footwear, hats and other clothing, durable consumer goods, room decorations, bed articles, household articles for daily use, furniture materials, medicine and medical services, transportation and miscellaneous commodities; the consumer goods of SA include education, cultural and recreational articles, and miscellaneous commodities; the consumer goods of LA include dining out, cultural and recreational articles, and miscellaneous commodities. Therefore, miscellaneous commodities were divided into three equal parts belonging to the three activities, respectively. The cultural and recreational articles were divided into two equal parts belonging to SA and LA, respectively. The ratio of the three consumer goods clusters can be employed to calculate the parameter II consumer expenditure distribution activities. The daily consumer expenditure cost per person (yuan/person/day) of the activities in the five provinces was calculated (Table 3). There were increasing trends for daily consumer expenditure for the three activities MA, SA and LA in the five provinces from 2003 to 2008, which meant that for a certain period, one consumed more expenditure in 2008 than in 2003. Accordingly, more production was brought more consumer expenditure. The residents of Guangdong consumed the most consumer goods at 50.57 yuan/person/day, followed by Zhejiang at 32.49 yuan/person/day, Hebei at 25.9 yuan/person/day, Sichuan at 24.22 yuan/ person/day, and Henan at 22.73 yuan/person/day. The reason for the variation among provinces was probably that residents perceived distinct consumer habits at different economic levels and regional disparities. The activity with the most daily consumer expenditure per person was MA, distantly followed by LA and SA in 2008 for each province. Due to the high value of consumer expenditure on MA, the more time that was spent on MA in one day, the higher the consumer expenditure for that day, far greater than time spent in LA and SA, and thereby creating more waste. The highest consumer expenditure of MA was closely connected to the definition of MA, which included many activities, such as house work, eating and drinking. An increased level of expenditure on LA represented a
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
6
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Table 3 Consumer expenditure distribution to activities in five provinces (yuan/person/day). Year
2003 2004 2005 2006 2007 2008
Zhejiang
Guangdong
Hebei
Henan
Sichuan
MA
SA
LA
MA
SA
LA
MA
SA
LA
MA
SA
LA
MA
SA
LA
18.72 19.20 19.37 19.89 20.63 23.15
3.58 3.56 3.44 3.52 3.95 4.10
3.66 3.72 3.69 3.86 4.36 5.24
21.62 23.85 26.62 28.66 33.63 38.33
4.02 4.03 4.08 4.60 4.73 4.68
5.07 5.34 5.74 6.75 6.81 7.56
11.93 12.96 14.70 16.62 18.46 21.05
1.70 1.81 1.97 2.10 2.14 2.32
1.10 1.34 1.70 2.07 2.30 2.53
11.73 12.19 12.86 14.36 16.07 17.73
1.82 1.88 2.10 2.17 2.14 2.22
1.41 1.51 1.99 2.25 2.26 2.78
11.57 12.35 13.41 15.14 17.33 19.70
2.02 1.97 2.00 2.18 2.15 1.85
1.77 1.97 2.20 2.57 2.47 2.67
1000 yuan = 160 dollars.
greater attention to recreation with economic development. By virtue of Parameter I and the inexorable and ever-increasing consumption, the quantity of waste production rose gradually according to waste statistics.
social models for gender roles, for example, in China, women were traditionally responsible for housework of MA, and men were less involved in domestic chores. 4.2. Model results
4.1.3. Parameter III: The ratio of time spent on different activities by residents in five provinces Parameter III is derived from the statistical analysis of the 2008 time-use survey designed and administered by the National Bureau of Statistics of China (2008). The use of time between male and female individuals on weekdays and weekends is shown in Fig. 5 split into MA, SA, and LA. Time distribution between the three activities of urban residents differed slightly among these five provinces. Urban residents in Hebei spent the most time on MA, not only for men on weekdays and weekends but also for women on weekdays and weekends. Urban resident in Zhejiang took the most time on SA for men on weekdays and women on weekdays and weekends. For time use in LA, Sichuan had the highest time for men and women on weekdays and weekends. Specifically, on weekdays, men spend much more time than women on SA and LA, which were approximately 80 and 35 min higher than woman on weekdays and 85 and 35 min on weekends. On the contrary, women spend much more time than men on MA, which was as high as 115 min on weekdays and 120 on weekends. Women spent much more time in MA then men, especially on weekends, which could be attributed to the
4.2.1. Waste generation by three activities in five provinces The waste generation from the three activities was determined by the product of waste generation per unit of consumer expenditure and consumer expenditure distribution to activities, in accordance with Eq. (2). The generation rates of waste to the three activities remained stable from 2003 to 2008, with a slight fluctuation (Fig. 6). The MSW generation of MA still consistently held primacy among the three activities during the study period in the five provinces in the following descending order: Guangdong (0.740 kg/per/day), Zhejiang (0.583 kg/per/day), Hebei (0.504 kg/ per/day), Henan (0.453 kg/person/day), and Sichuan (0.404 kg/ per/day), in 2008. The phenomenon was closely correlated to the highest consumer expenditure and highest time cost in MA. A similar phenomenon also appeared in other studies. The residential sectors with more MA were predicted to generate significantly more municipal solid waste than the commercial sectors in the
Fig. 5. Time distribution to three activities of urban residents in five provinces.
Fig. 6. MSW generation by different activities in five provinces (kg/person/day).
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Dublin region (Purcell and Magette, 2009). Food-related activities which were classified into MA influenced the amount of organic waste, which is the main fraction of ‘Kitchen’ wastes (Neset et al., 2006). MSW generation for SA in the five provinces remained constant or decreased in the following descending order: Zhejiang (0.103 kg/per/day), Guangdong (0.090 kg/per/day), Henan (0.057 kg/person/day), Hebei (0.056 kg/per/day), and Sichuan (0.038 kg/per/day), in 2008. MSW generation for LA rose gradually and has already caught up with SA with the following descending order: Guangdong (0.146 kg/per/day), Zhejiang (0.132 kg/per/day), Henan (0.071 kg/per/day), Hebei (0.061 kg/per/day), and Sichuan (0.055 kg/per/day), in 2008. The order of the five provinces in terms of waste in SA was in accordance with the per-capita GDP of the five provinces was as follows: Zhejiang (42,166 yuan/per/ year), Guangdong (38,748 yuan/per/year), Hebei (23,239 yuan/ per/year), Henan (19,593 yuan/per/year), and Sichuan (15,378 yuan/per/year), in 2008, except for Hebei and Henan. Since the time use survey samples were carried out in 2008, and the stable waste generation during the period 2003–2008 (range limit 67%), the results from 2008 were chosen for the further analysis (Fig. 7). Urban residents in the five provinces all produced the highest waste in MA (P70%), which is consistent with the above analysis. The percentage of the SA of the five provinces were less than or equal to that of LA, namely, Zhejiang (12% of SA, 16% of LA), Guangdong (9%, 15%), Hebei (10%, 10%), Henan (10%, 12%) and Sichuan (8%, 10%). Overall, the percentage waste in SA of the five provinces were approximately 10% while that of LA ranged from 10% to 16%. It is worthwhile that Zhejiang and Guangdong were also the top two provinces with the highest percentage of the product in tertiary industry to gross domestic product. 4.2.2. Waste generation from male and female on weekdays and weekends in the five provinces In terms of Eq. (3), the predicted quantity of waste generation from males and females on weekdays and weekends in the six years was calculated by Parameter III and split into the three activities. The waste generation of the four resident groups, including males and females, on weekdays and weekends (WeekdaysMale, Weekdays-Female, Weekends-Male and Weekends-Female) were obtained. These four groups’ daily waste generation trends during 2003 and 2008 were all similar to that in Fig. 2, with the same relative position every year. Taking 2008 as an example, for waste in MA, the descending order of these four
Fig. 7. Percentage of MSW production by different activities in five provinces in 2008.
7
groups was: Weekends-Female, Weekdays-Female, WeekendsMale, and Weekdays-Male. For waste in SA, the order was Weekdays-Male, Weekdays-Female, Weekends-Male, and Weekends-Female. For waste in LA, the order was: WeekendsMale, Weekends-Female, Weekdays-Male, and Weekdays-Female. For waste of the sum of the three activities, the order was Weekends-Female, Weekdays-Female, Weekends-Male, and Weekdays-Male, which is the same as that from MA. Generally, females produced more waste in MA, while males produced more waste in SA and LA, but overall, females produced more waste in total. Residents produced more waste in SA on weekdays while more waste in LA was produced on weekends, which was mainly due to different time use between weekdays and weekends. The capital daily MSW production by MA, SA and LA of the five groups in five provinces (Weekdays-Male, Weekdays-Female, Weekends-Male, and Weekends-Female) were shown in Fig. 8. In each activity, the five groups have a similar performance in the five provinces. In terms of MA, the descending order of the five provinces was Guangdong, Zhejiang, Hebei, Henan and Sichuan; as for SA, the order was Zhejiang, Guangdong, Hebei, Henan and Sichuan; for LA, the order was Guangdong, Zhejiang, Henan, Hebei and Sichuan. The order for SA was consistent with the order of capital GDP in these provinces. A linear model describes the relationship between capital GDP and waste generation in SA in 2008 of the five provinces (Fig. 9). A significant correlation between waste generation in SA and capital GDP was observed with a high coefficient of determination (0.97). The linear curve was found to be represented by Y ¼ 2E 06X þ 0:0062, where X is the capital GDP (yuan/per) and Y is the waste generation in SA (kg/per/day). Specific waste management policies for specific waste generators are helpful to achieve integrated waste management objectives. The above research will be valuable for waste management in these five provinces. Currently, the promotion of waste classification has been an important task for waste management. The characteristics of waste composition generated during various activities can help in drawing up more targeted management measures. The most effective way to reduce waste is to deal with it at source, from human daily activities (Tonglet et al., 2004).
Fig. 8. Capita daily MSW production by activities in five provinces in 2008.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
8
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
there are some small-sized markets not covered by formal statistics, such as certain small-sized food markets, and second hand markets for electronic devices on a semi-informal base. 5. Conclusions According to the newly developed model based on the relation between consumption and waste, this study obtained waste generation of particular activities of male, female in weekday or weekend for five provinces in China.
Fig. 9. The relationship between capital GDP and waste generation in SA in 2008.
Movement up the waste hierarchy brings waste policy into closer contact with household consumption practices (Gregson et al., 2013). Waste prevention can fundamentally reduce the waste from human activity, as defined by the organization for economic cooperation and development (OECD), and was positioned upstream from daily activities for residents (OECD, 2002). The current legislation has introduced quotas for recycling and recovery but hardly for waste prevention (Bartl, 2014). Waste prevention behaviors, such as using reusable packaging, reusable products, repairing before buying new items, reusing paper for writing notes, and reusing containers (Kurisu and Bortoleto, 2011), can all be encouraged to reduce waste. Waste prevention benefits could be derived from a package of measures, including, prevention targets, producer responsibility, householder charging, funding for pilot projects, collaboration between the public, private and third sectors, and public intervention campaigns (Cox et al., 2010). However, behavior patterns which lead to waste reduction are seldom socially oriented, seldom exposed to peer pressure, and very reliant on purely altruistic attitudes (Cecere et al., 2014). The usefulness of the theory of planned behavior and of Schwartz’s altruistic behavior model as bases were also confirmed for modeling participation in waste prevention (Bortoleto et al., 2012). 4.2.3. Limitations This study employed classification to quantify the relation between human activities and waste generation of China’s five provinces, providing important clues of waste prevention for policy maker. The data used is limited to the available data from NBS, and thus there are certain limitations here. The waste amount from NBS are officially measured in China at transfer stations, which are not fully reflecting the entire amounts of waste generated by the households. The differences of composition of waste and consumption habits in five provinces was ignored to develop this model. In terms of total retail sales of consumer goods, NBS adopts two methods of a comprehensive statistical method and sampling survey method. Data on basic conditions for all corporate enterprises of wholesale and retail trades above designated size, self-employed individuals, the establishments of other industries involved in the wholesale and retail trades, chain enterprises of wholesale and retail trades, large commodity markets with transaction value over 100 million yuan are collected through comprehensive reporting system. Data on enterprises and self-employed individuals below the designated size are collected by sample surveys. However,
(1) The consumption behavior during different human activities is the key factor of basis of MSW generation. Consumer goods, residents, activities could be described as activity subjects, the object, MSW generation media. The three factors corresponding to volume of retail sales of consumer goods, resident groups, and time spent on different activities by residents. (2) Three main factors at quantification were the waste generation per unit of consumer expenditure, consumer expenditure per unit of time spend on different activities, and the ratio of time spent on different activities by different resident groups. The waste generation per unit of consumer expenditure directly represented the quantitative relationship between consumer expenditure and MSW generation. (3) The waste generation in the five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) for the three activities have fluctuated slightly each year from 2003 to 2008. MSW generation of SA remained constant or decreased slightly, while that of LA remained constant or increased slightly in the five provinces. Urban residents in the five provinces all produced the highest waste in MA in 2008, accounting for P70% of total MSW generation. The percentage waste in SA between the five provinces had little variation (approximately 10%), compared to that of LA which had more obvious change (from 10% to 16%). (4) For total MSW generation and MSW generation in MA, the daily MSW generation by females is greater than that by males, and the daily MSW generation on weekends is far greater than that on weekdays. For MSW generation in SA, the daily MSW generation by males is greater than that by females and the MSW generation on weekdays is greater than that on weekends. For MSW generation in LA, the daily MSW generation by males is greater than that by females and the generation on weekends is greater than that on weekdays. (5) The parameter I of the waste generation per unit of consumer expenditure is inversely proportional to per-capita disposable income of urban residents. A linear model describes the relationship between capital GDP and waste generation in SA with a high coefficient of determination.
Acknowledgment This study is supported by National Science & Technology Pillar Program of China (2011BAJ07B04). The authors also thank the support of Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation. References Bartl, A., 2014. Moving from recycling to waste prevention: A review of barriers and enables. Waste Manage. Res. 32 (9), 3–18. Beigl, P., Sandra, L., Stefan, S., 2008. Modelling municipal solid waste generation: A review. Waste Manage. 28, 200–214.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx Benítez, S.O., Olvera, G.L., Morelos, R.A., Vega, C.A., 2008. Mathematical modeling to predict residential solid waste generation. Waste Manage. 28, S7–S13. Bhat, C.R., Koppelman, F.S., 1993. A conceptual framework of individual activity program generation. Transport. Res. Part A: Pol. Pract. 27, 433–446. Boldrin, A., Christensen, T.H., 2010. Seasonal generation and composition of garden waste in Aarhus (Denmark). Waste Manage. 30, 551–557. Bortoleto, A.P., Kurisu, K.H., Hanaki, K., 2012. Model development for household waste prevention behaviour. Waste Manage. 32 (12), 2195–2207. Cecere, G., Mancinelli, S., Mazzanti, M., 2014. Waste prevention and social preferences: The role of intrinsic and extrinsic motivations. Ecol. Econ. 107, 163–176. Chang, N.B., Lin, Y.T., 1997. An analysis of recycling impacts on solid waste generation by time series intervention modeling. Resour. Conserv. Recycl. 17, 165–186. Chen, H.W., Chang, N.B., 2000. Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resour. Conserv. Recycl. 17 (3 29), 1–18. Cox, J., Giorgi, S., Sharp, V., Strange, K., Wilson, D.C., Blakey, N., 2010. Household waste prevention - a review of evidence. Waste Manage. Res. 28 (3), 193–219. Dennison, G.J., Dodd, V.A., Whelan, B., 1996a. A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland, I. Waste Compos. Resour. Conserv. Recycl. 17, 227–244. Dennison, G.J., Dodd, V.A., Whelan, B., 1996b. A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland, II. Resour. Conserv. Recycl. 17, 245–257. Dyson, B., Chang, N.B., 2005. Forecasting municipal solid waste generation in a fastgrowing urban region with system dynamics modeling. Waste Manage. 25, 669–679. Fu, H.Z., Ho, Y.S., Sui, Y.M., Li, Z.S., 2010. A bibliometric analysis of solid waste research during the period 1993–2008. Waste Manage. 30, 2410–2417. Gay, A.E., Beam, T.G., Brian, W.M., 1993. Cost-effective solid-waste characterization methodology. J. Environ. Eng. 119, 631–644. Gregson, N., Crang, M., Laws, J., Fleetwood, T., Holmes, H., 2013. Moving up the waste hierarchy: Car boot sales, reuse exchange and the challenges of consumer culture to waste prevention. Resour. Conserv. Recycl. 77, 97–107. Hockett, D., Lober, D.J., Pilgrim, K., 1995. Determinants of per capita municipal solid waste generation in the Southeastern United States. J. Environ. Manage. 45, 205–217. Ibiebele, D.D., 1986. Rapid method for estimating solid wastes generation rate in developing countries. Waste Manage. Res. 4, 361–365. Jara-Diaz, S., 2003. On the goods-activities technical relations in the time allocation theory. Transportation 30, 245–260. Joosten, L.A.J., Hekkert, M.P., Worrell, E., 2000. Assessment of the plastic flows in the Netherlands using STREAMS. Resour. Conserv. Recycl. 30, 135–161.
9
Kurisu, K.H., Bortoleto, A.P., 2011. Comparison of waste prevention behaviors among three Japanese megacity regions in the context of local measures and socio-demographics. Waste Manage. 31, 1441–1449. Leao, S., Bishop, I., Evans, D., 2001. Assessing the demand of solid waste disposal in urban region by urban dynamics modeling in a GIS environment. Resour. Conserv. Recycl. 33, 289–313. Li, Z.S., Fu, H.Z., Qu, X.Y., 2011. Estimating municipal solid waste generation by different activities and various resident groups: A case study of Beijing. Sci. Total Environ. 409, 4406–4414. Louis, G.E., 2004. A historical context of municipal solid waste management in the United States. Waste Manage. Res. 22, 306–322. Mazars, 2003. Dublin City Council Waste Management Plan Review Report, Mazars Ltd., Dublin, Ireland. Moriwaki, H., Kitajima, S., Katahira, K., 2009. Waste on the roadside, ‘poi-sute’ waste: Its distribution and elution potential of pollutants into environment. Waste Manage. 29, 1192–1197. National Bureau of Statistics of China., 2008. China Time Use Survey Report 2008, National Bureau of Statistics. (in Chinese). . Neset, T.S.S., Bader, H.P., Scheidegger, R., 2006. Food consumption and nutrient flows – Nitrogen in Sweden since the 1870s. J. Ind. Ecol. 10, 61–75. OECD, 2002. Working Group on Waste Prevention and Recycling, Working Group on Environmental Information and Outlooks: OECD Workshop on Waste Prevention: Toward Performance Indicators 8–10 October 2001, Environment Policy Committee OECD Headquarters, Paris, pp. 1–237. Parfitt, J.P., Lovett, A.A., Sünnenberg, G., 2001. Classification of local authority waste collection and recycling strategies in England and Wales. Resour. Conserv. Recycl. 32, 239–257. Patel, M.K., Jochem, E., Radgen, P., Worrell, E., 1998. Plastic streams in Germany - an analysis of production, consumption and waste generation. Resour. Conserv. Recycl. 24, 191–215. Purcell, M., Magette, W.L., 2009. Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region. Waste Manage. 29, 1237–1250. Thøgersen, J., 1996. Wasteful food consumption: trends in food and packaging waste. Scand. J. Manag. 12, 291–304. Tonglet, M., Phillips, P.S., Bates, M.P., 2004. Determining the drivers for householder pro-environmental behaviour: waste minimisation compared to recycling. Resour. Conserv. Recycl. 42, 27–48. Yenice, M.K., Dogruparmak, S.C., Durmusoglu, E., Ozbay, B., Oz, H.O., 2011. Solid waste characterization of Kocaeli. Pol. J. Environ. Stud. 20, 479–484.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
Contents lists available at ScienceDirect
Waste Management journal homepage: www.elsevier.com/locate/wasman
Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China Hui-zhen Fu, Zhen-shan Li ⇑, Rong-hua Wang Department of Environmental Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
a r t i c l e
i n f o
Article history: Received 19 November 2014 Accepted 17 March 2015 Available online xxxx Keywords: Estimate Waste generation Activity Resident group
a b s t r a c t The quantities and composition of municipal solid waste (MSW) are important factors in the planning and management of MSW. Daily human activities were classified into three groups: maintenance activities (meeting the basic needs of food, housing and personal care, MA); subsistence activities (providing the financial support requirements, SA); and leisure activities (social and recreational pursuits, LA). A model, based on the interrelationships of expenditure on consumer goods, time distribution, daily activities, residents groups, and waste generation, was employed to estimate MSW generation by different activities and resident groups in five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) of China. These five provinces were chosen for this study and the distribution patterns of MSW generated by different activities and resident groups were revealed. The results show that waste generation in SA and LA fluctuated slightly from 2003 to 2008. For general waste generation in the five provinces, MA accounts for more than 70% of total MSW, SA approximately 10%, and LA between 10% and 16% by urban residents in 2008. Females produced more daily MSW than males in MA. Males produced more daily MSW than females in SA and LA. The wastes produced at weekends in MA and LA were far greater than on weekdays, but less than on weekdays for SA wastes. Furthermore, one of the model parameters (the waste generation per unit of consumer expenditure) is inversely proportional to per-capita disposable income of urban residents. A significant correlation between gross domestic product (GDP) and waste generation by SA was observed with a high coefficient of determination. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Solid waste research has attracted increasing attentions in recent decades, while solid waste management was one of the hot issues based on the statistical analysis of author keywords (Fu et al., 2010). The prediction of municipal solid waste (MSW) generation plays an important role in solid waste management (Dyson and Chang, 2005). The amount and composition of waste generation provides the basic information needed for the planning, operation and optimization of waste management systems (Beigl et al., 2008). Modeling is a tool used for the planning and management of municipal waste (Benítez et al., 2008). Conventional prediction models include correlation analysis models (Dennison et al., 1996a,b), multiple regression analysis models (Hockett et al., 1995), Single regression analysis models (Thøgersen, 1996), group comparison (Parfitt et al., 2001), system dynamics (Dyson ⇑ Corresponding author at: No. 5, Yi Heyuan Road, Haidian District, Department of Environmental Engineering, Peking University, Beijing 100871, China. Tel.: +86 10 6275 3962; fax: +86 10 6275 6526. E-mail address: [email protected] (Z.-s. Li).
and Chang, 2005), time series analysis (Leao et al., 2001), gray fuzzy dynamic models (Chang and Lin, 1997; Chen and Chang, 2000), and input–output analysis models (Gay et al., 1993; Patel et al., 1998; Joosten et al., 2000). Estimation models play an important role in predicting the MSW amount and composition, but most of these models are incapable of describing the waste generation if the waste is generated by different activities and is from different resident groups. Modeling the waste generation distribution is a useful method to anticipate the design of waste management strategies as a function of consumption and demographic development, and to plan for future management needs (Purcell and Magette, 2009). Researchers examined waste generation from many different angles, especially in recent years. They explored rapid methods for estimating the rate of solid waste generation (Ibiebele, 1986), identified the waste spatial distribution (Purcell and Magette, 2009; Yenice et al., 2011), waste composition distribution (Moriwaki et al., 2009), and time-related waste distribution (Boldrin and Christensen, 2010), to aid more targeted waste planning and policy decisions.
http://dx.doi.org/10.1016/j.wasman.2015.03.029 0956-053X/Ó 2015 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
2
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
As a whole, when compared with existing predictions or estimates for MSW generation, the estimation of MSW distribution by different activities and resident groups at a city level, in this paper presents a novel and reliable view for the analysis of MSW generation. Moreover, it is difficult to obtain the amount of waste generated by different activities and resident groups by field surveys, because it is hard to measure waste generation by different resident groups in different time. Recently, a new model based on the relationship of consumption and waste was recently proposed to describe the MSW generation in different activities and by different resident groups (Li et al., 2011). This study employed this model to present waste generation by different resident groups in different time for a wider area of five provinces in China. Five provinces which have top economic development levels and different geographical positions were chosen to study the differences in waste generation for each activity of male and female residents on weekday and weekend. This will help the development of waste management policies and understand MSW generation patterns beyond the current prediction techniques. Furthermore, a high correlation coefficient was observed between the parameters of the newly developed model and economic related indicator. 2. Model Waste generation has long been a consequence of human activity (Louis, 2004). It is the result of a production and consumption cycle which starts with the production of consumer goods and continues with the generation, collection, storage, and final disposal of MSW. All manufactured, commercialized and consumed products are finally converted, or at least partially converted, into waste (Benítez et al., 2008). A new model based on the relationship of consumption and waste was proposed to describe the MSW generation in different activities and by different resident groups (Li et al., 2011). The conceptual model and mathematical model were presented sequentially. 2.1. Conceptual model The essential reason for MSW generation is the consumption of consumer goods in human beings’ daily activities. Consumption requires time, and human activities consume goods (Jara-Diaz, 2003). Consumer goods eventually turn into disposal in part (Benítez et al., 2008). People play an important role as the users who utilized consumer goods and the producers of waste. Therefore, the concrete relationships between residents and MSW generation in a city can be divided into four levels: resident groups, consumer goods, activity and waste composition, as shown in Fig. 1 (Li et al., 2011). Each level fell into groups, and the line relationships turn to complex distributions between adjacent levels. All the people in the city were divided into Rn groups (R1, R2, . . ., Ri, . . ., Rn). Each group of residents conduct An groups of activities (A1, A2, . . ., Ai, . . ., An), and in each activity group, Cn groups of consumer goods (C1, C2, . . ., Ci, . . ., Cn) were consumed and each group of consumer goods finally converted to Wn groups of waste composition (W1, W2, . . .,Wi, . . ., Wn). Ultimately, the total quantity of MSW is the sum of various waste groups derived from consumer goods in different activities by all people in the city. The important relationships were obviously involved, between resident group and activities, between activities and consumer goods, and between consumer goods and waste composition. Three important parameters were therefore proposed based on these three relationships to describe the waste generation.
2.2. Mathematical model Initially, two assumptions were proposed (Li et al., 2011): (1) The research target of the model was the group behavior instead of individual behavior. Residents, consumer goods, and waste were classified into groups. Thus, the gender, age, education, and other personal features of the actors were not included in the model. (2) The quantity of waste generation was only determined by consumer expenditure. All the consumer goods consumed in one year were finally converted into MSW. The consumer goods of one group share the same rate of conversion. The quantity of waste generation is directly proportional to consumer expenditure and the waste generation per unit consumer expenditure can be considered constant in a short time (i.e. > > = < 1> k2 (1) K i ¼ ; ki , waste generation per unit of consumer >...> > > ; : ki expenditure in year i. (2) C j ¼ f c1 c2 . . . cj g; cj , consumer expenditure distribution to activity j in unit time. 9 8 > > > r11 r12 . . . r1j > = < r21 r22 . . . r2j ; r , waste generation rate of (3) Rij ¼ > ij > ... ... ... ...> > ; : ri1 ri2 . . . rij activity j in year i.
Males and females were the two residents groups analyzed here (m = 1, stands for male; m = 2, stands for female). Then, waste production by resident group m in activity j in year i can be calculated by time spent on activity j by resident group m (t mj ) and the waste generation rate of activity j in year i (r ij ) based on Eq. (3):
xmj ¼ tmj r ij where
ð3Þ
t 11 t 12 . . . t1j ; tmj , time assigned to activity j t 21 t 22 . . . t2j by resident group m. x11 x12 . . . x1j ; xmj , waste generation rate of (2) X mj ¼ x21 x22 . . . x2j activity j by resident group m in year i. (1) T mj ¼
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
3
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Resident group
R1
R2
Ri
Rn
...
...
A1 Ai An
Activity
C1 Ci
Cn
...
...
...
...
An
...
...
...
...
...
... ...
...
W1 Wi Wn W1 Wi Wn
...
...
...
Waste compostion
Cn
...
C1 Ci Cn C1 Ci
...
...
...
Consumer goods
A1 Ai An A1 Ai
...
W1 Wi Wn W1 Wi Wn
Parameter III Parameter II Parameter I
All the People in a City
Time assignment to activities by resident groups
Consumer expenditure distribution to activities
Waste generation per unit consumer expenditure
Total Municipal solid waste in a City Fig. 1. Structure of MSW generation in a city.
3. Data source and data processing 3.1. Waste generation of Study area The waste generation was related to consumption (Benítez et al., 2008), and thus the five provinces of China (Zhejiang in eastern China, Guangdong in southern China, Hebei in northern China, Henan in the Central China area, and Sichuan in Southwest China) with higher gross regional product of China were chosen for further analysis. Table 1 lists the basic demographics and economical information of these five provinces in 2008. The yuan, the monetary unit of China, was used as the unit of consumer expenditure for detailed calculations. Guangdong have the highest gross regional product of 1886.4 billion yuan in 2008 according to the National Bureau of Statistics (NBS) of China. Guangdong also hold the greatest populations, while there were the smallest populations of Zhejiang. There were more males than females in all of these five provinces. Sichuan had the smallest percentage of the persons aged 15–64 and the least gross regional product of 1250.6 billion yuan. Five provinces had different percentages of product from three strata of industry to gross domestic product (GDP). Fig. 2 shows the waste production of the urban area in the five provinces during 2003–2008. Guangdong produced the most waste in China in 2008 and in the period of 2003–3008, and the waste production kept increasing during 2003–3008. Sichuan contributed the least waste in these five provinces. This performance is consistent to its small gross regional product, which confirmed the assumption that waste generation is related to economic level. The waste production of Zhejiang, Henan and Hebei fluctuated similarly.
Fig. 2. Waste production of five provinces during 2003–2008.
3.2. Volume of retail sales of consumer goods As GDP has increased, so has the volume of waste produced, because economic prosperity affects waste generation by stimulating increased consumer activity and business expansion (Mazars, 2003). Based the newly developed model, the consumer expenditure could be used for the Parameter II consumer expenditure distribution to activities. The consumer expenditure data set for this paper was obtained from the statistical yearbook (2003–2008) for these five provinces administered by the National Bureau of Statistics. The statistical data did not consider inflation and
Table 1 Demographics and economical information of five provinces in 2008. Province
Zhejiang Guangdong Hebei Henan Sichuan
UR
2700.2 5241.3 2928.0 3573.0 3041.6
GRP
2148.7 3569.6 1618.8 1840.8 1250.6
Sex ratio
1.039 1.051 1.040 1.023 1.026
% Aged 0–14
Aged 15–64
Aged > 65
PI
SI
TI
14.0 18.5 16.1 19.8 17.4
75.3 74.0 75.2 72.3 71.2
10.7 7.56 8.74 7.82 11.4
5.1 5.5 12.6 14.4 18.9
53.9 51.6 54.2 56.9 46.3
41 42.9 33.2 28.6 34.8
UR: Urban residents (Unit: 104 persons); GRP: Gross regional product (Unit: billion yuan); Sex ratio: The sex ratio of male to female; PI: the percentage of the product in primary industry to gross domestic product; SI: the percentage of the product for secondary industry to the gross domestic product; TI: the percentage of the product in tertiary industry to gross domestic product; 1000 yuan = 160 dollars.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
4
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Table 2 Total volume of retail sales of consumer goods by urban residents in five provinces. Year
2003 2004 2005 2006 2007 2008
Zhejiang
Guangdong
Hebei
Henan
Sichuan
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
RPI
U/R
UVRS
113.4 113.8 110.9 109.9 109.0 105.0
2.5 2.7 2.6 2.5 2.5 2.4
2285.6 2388.9 2493.3 2604.5 2815.4 3202.6
100 102.9 101.8 101.5 103.4 106
3.4 3.4 3.5 3.5 3.9 3.8
4934.9 5489.7 6376.8 7404.9 8490.3 9674.3
100.2 103.2 101.1 101.5 104.1 106.7
2.5 2.5 2.6 2.7 2.7 2.8
1219.7 1401.7 1730.6 2012.3 2335.6 2769
101.6 105.4 102.1 101.3 105.4 107.5
3.6 3.4 3.4 3.4 3.4 3.4
1437.1 1598 1852.1 2185.8 2531.7 2964.8
100.1 103.7 100.6 101.7 105.3 105.3
3.3 3.2 3.1 3.2 3.2 3.2
1341.0 1499.1 1729.5 2000.5 2280.6 2688.2
RPI, Retail Price Index; U/R, ratio of consumption level of urban residents to rural residents; UVRS, urban volume of retail sales (Unit: 108 yuan); 1000 yuan = 160 dollars.
covered both the rural and urban area. Therefore, some pretreatment needed to be conducted to obtain the consumer expenditure of urban area. Firstly, to eliminate the effects of the inflation rate for the total volume of retail sales of consumer goods, the Retail Price Index (RPI), which reflects the general change in retail prices of commodities was employed. Secondly, the ratio of the consumption level of urban residents to rural residents (U/R) and the population of urban and rural areas were also developed to calculate the total volume of retail sales of consumer goods in urban areas (Urban Volume of Retail Sales, UVRS). Retail price index, ratio of consumption level of urban residents to rural residents, and urban volume of retail sales are displayed in Table 2. After the correction by retail price index and deduction of rural consumer expenditure, urban volume of retail sales (UVRS) are shown in Table 2, increasing steadily from 2003 to 2008 for the five provinces. In comparison to waste generation, the consumer expenditure of Guangdong was also the most among these five provinces. The fact that Guangdong had the most population, followed distantly by other four provinces, might be one of the reasons. The least waste in Sichuan could partly explain the least volume of retail sales of consumer goods of it. 3.3. Time allocation among the three basic activities The time allocation were derived from the 2008 time-use survey designed and administered by the National Bureau of Statistics (2008), administered in China. This survey was initiated on May 2008, and was the first time survey of the activities of Chinese individuals by Chinese government. The time survey provides time use information for these five provinces (Zhejiang, Guangdong, Hebei, Henan, and Sichuan). The urban respondents recorded daily life in a diary. In addition, they were asked to record their activities during 144, 10-min time slices over the period of the day. If more than one type of activity was pursued during the time slice, all the activities were recorded by the respondent. The respondent marked the primary activity and the secondary one. Respondents recorded the activity under one of the several detailed pre-coded activity types. These pre-coded activities included personal activity; paid work; primary production activities not for establishments; building and extensions of dwelling; services for income and other production of goods not for establishments; housework; caring for and helping household or nonhousehold members; education and training, recreation and social interaction; and other activities (not classified elsewhere). The respondent recorded the time allocation information over any two days, where one is a weekday and the other is the weekend. They recorded the time distribution of activities over two days. Then the time of each equivalent activity was calculated by the averages of weight (the weekdays account for 5/7 while the weekends for 2/7). The result is the time allocated to one activity. The whole data production process was affected by the classification used, and therefore the quality of the resulting figures depends on how well this classification reflected the types of
activities carried out by the population. The activities were classified into three categories, subsistence activity, maintenance activity, and leisure activity, which were first identified by Bhat and Koppelman (1993). According to this classification, subsistence activity referred to the supply of work or work-related business, and was essential for providing the financial requirements for pursuing maintenance and leisure activities. Maintenance activity pertained to the purchase and consumption of goods/services to satisfy household/personal physiological needs and biological needs. Leisure activity referred to social and recreational pursuits motivated by cultural and psychological needs. The detailed categories of activities are shown as follows: (1) maintenance activities (MA) included the following activities: housework; caring for and helping household or non-household members; community service; public benefit activities; eating; drinking; smoking; sleeping; personal care activities; and related transport activities. (2) subsistence activities (SA) included the following activities: work; workrelated activities; family primary production activities; family manufacturing activities; construction activities; family service operations; education; training activities; and work-related transport activities. (Students were a special group in this study, because they did not have subsistence time. The students’ duties were learning, so the learning time belonged to this category in the study.) (3) leisure activities (LA) included the following activities: TV viewing; reading, listening to the radio; sport/hobbies; entertainment; and socializing. The average time of the urban individuals engaged in SA, MA, and LA in one day was calculated. The time allocated to all the activities in a single day did not total 24 h, because the sleeping time was excluded. We therefore used the percentage of time allocated to one activity to represent the time allocated to the activity. 4. Results and discussion The data investigated in Section 3 were substituted in Eq. (2), and then the three parameters of the newly developed model including Parameter I: the waste generation per unit of consumer expenditure, Parameter II: consumer expenditure distribution to activities, and Parameter III: The ratio of time spent on different activities by residents in five provinces could be obtained. 4.1. Parameter calculation 4.1.1. Parameter I: The waste generation per unit of consumer expenditure The annual waste generation divided by total volume of retail sales of consumer goods equals parameter I – waste generation per unit of consumer expenditure (ki ), in accordance with above assumption, suggesting the conversion of consumer expenditure to waste. The greater the ki , the greater the waste produced from the same consumer goods. The waste generation per unit of consumer expenditure in Zhejiang, Guangdong, Henan, Hebei and Sichuan are displayed in Fig. 3. ki displayed different downward
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Fig. 3. Waste generation per unit of consumer expenditure in five provinces (kg/ yuan).
trends of these five provinces from 2003 to 2008. In Hebei, the ki parameter decreased from 0.058 kg/yuan in 2003 to 0.024 kg/yuan in 2008, in Henan it fell from 0.045 kg/yuan to 0.026 kg/yuan and in Sichuan it fell more rapidly from 0.041 kg/yuan to 0.020 kg/yuan compared with Guangdong and Zhejiang with falls of 0.029 kg/ yuan to 0.019 kg/yuan and 0.030 kg/yuan to 0.025 kg/yuan, respectively. It is also noticed that the gap between these five provinces has reduced from 0.029 kg/yuan in 2003 to 0.007 kg/yuan in 2008. Although Guangdong produced the most waste and consumer expenditure, it has the least waste generation per unit of consumer expenditure. This might be partly attributed to the largest percentage (42.9%) of the product in tertiary industry to GDP (Table 1). The per-capita disposable income of urban residents is closely related to the retail sales of consumer goods, and therefore the waste production. The relationship linking per-capita disposable income of urban residents with waste generation per unit of consumer expenditure was capable of being expressed by the mathematical formula:
0:0028 ¼
1 K I
It was also expressed as:
K I ¼ 357 where K is waste generation per unit of consumer expenditure, I per-capita disposable income of urban residents. The product between K and I was almost constant, which indicated that the more per-capita disposable income of urban residents, the lower the waste generation per unit of consumer expenditure. It could be partly explained by the increasing importance of the inner quality of consumer goods to high income people. Based on the same consumer expenditure, the high income people would produce less waste than the low income people. That was why the reciprocal of per-capita disposable income of urban residents is proportional to waste generation per unit of consumer expenditure (Fig. 4). 4.1.2. Parameter II: Consumer expenditure distribution to activities Activity has a significant connection which links consumer goods to waste generation. The weight distribution of consumer expenditure to the three activities was calculated by consumer goods cluster. People consume various consumer goods when involved in different activities. Depending on the activity category, all consumer items from statistical books were classified
5
Fig. 4. Relationship between per-capita disposable income of urban residents and waste generation per unit of consumer expenditure.
into three categories. The consumer goods of MA contains grain, starches and tubers, oil and fats, meat, poultry and related products, eggs, aquatic products, vegetables, condiments, sugar, tobacco, liquor and beverages, dried and fresh melons and fruits, nuts and kernels, cake, milk and dairy products, other food, garments, clothing materials, footwear, hats and other clothing, durable consumer goods, room decorations, bed articles, household articles for daily use, furniture materials, medicine and medical services, transportation and miscellaneous commodities; the consumer goods of SA include education, cultural and recreational articles, and miscellaneous commodities; the consumer goods of LA include dining out, cultural and recreational articles, and miscellaneous commodities. Therefore, miscellaneous commodities were divided into three equal parts belonging to the three activities, respectively. The cultural and recreational articles were divided into two equal parts belonging to SA and LA, respectively. The ratio of the three consumer goods clusters can be employed to calculate the parameter II consumer expenditure distribution activities. The daily consumer expenditure cost per person (yuan/person/day) of the activities in the five provinces was calculated (Table 3). There were increasing trends for daily consumer expenditure for the three activities MA, SA and LA in the five provinces from 2003 to 2008, which meant that for a certain period, one consumed more expenditure in 2008 than in 2003. Accordingly, more production was brought more consumer expenditure. The residents of Guangdong consumed the most consumer goods at 50.57 yuan/person/day, followed by Zhejiang at 32.49 yuan/person/day, Hebei at 25.9 yuan/person/day, Sichuan at 24.22 yuan/ person/day, and Henan at 22.73 yuan/person/day. The reason for the variation among provinces was probably that residents perceived distinct consumer habits at different economic levels and regional disparities. The activity with the most daily consumer expenditure per person was MA, distantly followed by LA and SA in 2008 for each province. Due to the high value of consumer expenditure on MA, the more time that was spent on MA in one day, the higher the consumer expenditure for that day, far greater than time spent in LA and SA, and thereby creating more waste. The highest consumer expenditure of MA was closely connected to the definition of MA, which included many activities, such as house work, eating and drinking. An increased level of expenditure on LA represented a
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
6
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Table 3 Consumer expenditure distribution to activities in five provinces (yuan/person/day). Year
2003 2004 2005 2006 2007 2008
Zhejiang
Guangdong
Hebei
Henan
Sichuan
MA
SA
LA
MA
SA
LA
MA
SA
LA
MA
SA
LA
MA
SA
LA
18.72 19.20 19.37 19.89 20.63 23.15
3.58 3.56 3.44 3.52 3.95 4.10
3.66 3.72 3.69 3.86 4.36 5.24
21.62 23.85 26.62 28.66 33.63 38.33
4.02 4.03 4.08 4.60 4.73 4.68
5.07 5.34 5.74 6.75 6.81 7.56
11.93 12.96 14.70 16.62 18.46 21.05
1.70 1.81 1.97 2.10 2.14 2.32
1.10 1.34 1.70 2.07 2.30 2.53
11.73 12.19 12.86 14.36 16.07 17.73
1.82 1.88 2.10 2.17 2.14 2.22
1.41 1.51 1.99 2.25 2.26 2.78
11.57 12.35 13.41 15.14 17.33 19.70
2.02 1.97 2.00 2.18 2.15 1.85
1.77 1.97 2.20 2.57 2.47 2.67
1000 yuan = 160 dollars.
greater attention to recreation with economic development. By virtue of Parameter I and the inexorable and ever-increasing consumption, the quantity of waste production rose gradually according to waste statistics.
social models for gender roles, for example, in China, women were traditionally responsible for housework of MA, and men were less involved in domestic chores. 4.2. Model results
4.1.3. Parameter III: The ratio of time spent on different activities by residents in five provinces Parameter III is derived from the statistical analysis of the 2008 time-use survey designed and administered by the National Bureau of Statistics of China (2008). The use of time between male and female individuals on weekdays and weekends is shown in Fig. 5 split into MA, SA, and LA. Time distribution between the three activities of urban residents differed slightly among these five provinces. Urban residents in Hebei spent the most time on MA, not only for men on weekdays and weekends but also for women on weekdays and weekends. Urban resident in Zhejiang took the most time on SA for men on weekdays and women on weekdays and weekends. For time use in LA, Sichuan had the highest time for men and women on weekdays and weekends. Specifically, on weekdays, men spend much more time than women on SA and LA, which were approximately 80 and 35 min higher than woman on weekdays and 85 and 35 min on weekends. On the contrary, women spend much more time than men on MA, which was as high as 115 min on weekdays and 120 on weekends. Women spent much more time in MA then men, especially on weekends, which could be attributed to the
4.2.1. Waste generation by three activities in five provinces The waste generation from the three activities was determined by the product of waste generation per unit of consumer expenditure and consumer expenditure distribution to activities, in accordance with Eq. (2). The generation rates of waste to the three activities remained stable from 2003 to 2008, with a slight fluctuation (Fig. 6). The MSW generation of MA still consistently held primacy among the three activities during the study period in the five provinces in the following descending order: Guangdong (0.740 kg/per/day), Zhejiang (0.583 kg/per/day), Hebei (0.504 kg/ per/day), Henan (0.453 kg/person/day), and Sichuan (0.404 kg/ per/day), in 2008. The phenomenon was closely correlated to the highest consumer expenditure and highest time cost in MA. A similar phenomenon also appeared in other studies. The residential sectors with more MA were predicted to generate significantly more municipal solid waste than the commercial sectors in the
Fig. 5. Time distribution to three activities of urban residents in five provinces.
Fig. 6. MSW generation by different activities in five provinces (kg/person/day).
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
Dublin region (Purcell and Magette, 2009). Food-related activities which were classified into MA influenced the amount of organic waste, which is the main fraction of ‘Kitchen’ wastes (Neset et al., 2006). MSW generation for SA in the five provinces remained constant or decreased in the following descending order: Zhejiang (0.103 kg/per/day), Guangdong (0.090 kg/per/day), Henan (0.057 kg/person/day), Hebei (0.056 kg/per/day), and Sichuan (0.038 kg/per/day), in 2008. MSW generation for LA rose gradually and has already caught up with SA with the following descending order: Guangdong (0.146 kg/per/day), Zhejiang (0.132 kg/per/day), Henan (0.071 kg/per/day), Hebei (0.061 kg/per/day), and Sichuan (0.055 kg/per/day), in 2008. The order of the five provinces in terms of waste in SA was in accordance with the per-capita GDP of the five provinces was as follows: Zhejiang (42,166 yuan/per/ year), Guangdong (38,748 yuan/per/year), Hebei (23,239 yuan/ per/year), Henan (19,593 yuan/per/year), and Sichuan (15,378 yuan/per/year), in 2008, except for Hebei and Henan. Since the time use survey samples were carried out in 2008, and the stable waste generation during the period 2003–2008 (range limit 67%), the results from 2008 were chosen for the further analysis (Fig. 7). Urban residents in the five provinces all produced the highest waste in MA (P70%), which is consistent with the above analysis. The percentage of the SA of the five provinces were less than or equal to that of LA, namely, Zhejiang (12% of SA, 16% of LA), Guangdong (9%, 15%), Hebei (10%, 10%), Henan (10%, 12%) and Sichuan (8%, 10%). Overall, the percentage waste in SA of the five provinces were approximately 10% while that of LA ranged from 10% to 16%. It is worthwhile that Zhejiang and Guangdong were also the top two provinces with the highest percentage of the product in tertiary industry to gross domestic product. 4.2.2. Waste generation from male and female on weekdays and weekends in the five provinces In terms of Eq. (3), the predicted quantity of waste generation from males and females on weekdays and weekends in the six years was calculated by Parameter III and split into the three activities. The waste generation of the four resident groups, including males and females, on weekdays and weekends (WeekdaysMale, Weekdays-Female, Weekends-Male and Weekends-Female) were obtained. These four groups’ daily waste generation trends during 2003 and 2008 were all similar to that in Fig. 2, with the same relative position every year. Taking 2008 as an example, for waste in MA, the descending order of these four
Fig. 7. Percentage of MSW production by different activities in five provinces in 2008.
7
groups was: Weekends-Female, Weekdays-Female, WeekendsMale, and Weekdays-Male. For waste in SA, the order was Weekdays-Male, Weekdays-Female, Weekends-Male, and Weekends-Female. For waste in LA, the order was: WeekendsMale, Weekends-Female, Weekdays-Male, and Weekdays-Female. For waste of the sum of the three activities, the order was Weekends-Female, Weekdays-Female, Weekends-Male, and Weekdays-Male, which is the same as that from MA. Generally, females produced more waste in MA, while males produced more waste in SA and LA, but overall, females produced more waste in total. Residents produced more waste in SA on weekdays while more waste in LA was produced on weekends, which was mainly due to different time use between weekdays and weekends. The capital daily MSW production by MA, SA and LA of the five groups in five provinces (Weekdays-Male, Weekdays-Female, Weekends-Male, and Weekends-Female) were shown in Fig. 8. In each activity, the five groups have a similar performance in the five provinces. In terms of MA, the descending order of the five provinces was Guangdong, Zhejiang, Hebei, Henan and Sichuan; as for SA, the order was Zhejiang, Guangdong, Hebei, Henan and Sichuan; for LA, the order was Guangdong, Zhejiang, Henan, Hebei and Sichuan. The order for SA was consistent with the order of capital GDP in these provinces. A linear model describes the relationship between capital GDP and waste generation in SA in 2008 of the five provinces (Fig. 9). A significant correlation between waste generation in SA and capital GDP was observed with a high coefficient of determination (0.97). The linear curve was found to be represented by Y ¼ 2E 06X þ 0:0062, where X is the capital GDP (yuan/per) and Y is the waste generation in SA (kg/per/day). Specific waste management policies for specific waste generators are helpful to achieve integrated waste management objectives. The above research will be valuable for waste management in these five provinces. Currently, the promotion of waste classification has been an important task for waste management. The characteristics of waste composition generated during various activities can help in drawing up more targeted management measures. The most effective way to reduce waste is to deal with it at source, from human daily activities (Tonglet et al., 2004).
Fig. 8. Capita daily MSW production by activities in five provinces in 2008.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
8
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx
there are some small-sized markets not covered by formal statistics, such as certain small-sized food markets, and second hand markets for electronic devices on a semi-informal base. 5. Conclusions According to the newly developed model based on the relation between consumption and waste, this study obtained waste generation of particular activities of male, female in weekday or weekend for five provinces in China.
Fig. 9. The relationship between capital GDP and waste generation in SA in 2008.
Movement up the waste hierarchy brings waste policy into closer contact with household consumption practices (Gregson et al., 2013). Waste prevention can fundamentally reduce the waste from human activity, as defined by the organization for economic cooperation and development (OECD), and was positioned upstream from daily activities for residents (OECD, 2002). The current legislation has introduced quotas for recycling and recovery but hardly for waste prevention (Bartl, 2014). Waste prevention behaviors, such as using reusable packaging, reusable products, repairing before buying new items, reusing paper for writing notes, and reusing containers (Kurisu and Bortoleto, 2011), can all be encouraged to reduce waste. Waste prevention benefits could be derived from a package of measures, including, prevention targets, producer responsibility, householder charging, funding for pilot projects, collaboration between the public, private and third sectors, and public intervention campaigns (Cox et al., 2010). However, behavior patterns which lead to waste reduction are seldom socially oriented, seldom exposed to peer pressure, and very reliant on purely altruistic attitudes (Cecere et al., 2014). The usefulness of the theory of planned behavior and of Schwartz’s altruistic behavior model as bases were also confirmed for modeling participation in waste prevention (Bortoleto et al., 2012). 4.2.3. Limitations This study employed classification to quantify the relation between human activities and waste generation of China’s five provinces, providing important clues of waste prevention for policy maker. The data used is limited to the available data from NBS, and thus there are certain limitations here. The waste amount from NBS are officially measured in China at transfer stations, which are not fully reflecting the entire amounts of waste generated by the households. The differences of composition of waste and consumption habits in five provinces was ignored to develop this model. In terms of total retail sales of consumer goods, NBS adopts two methods of a comprehensive statistical method and sampling survey method. Data on basic conditions for all corporate enterprises of wholesale and retail trades above designated size, self-employed individuals, the establishments of other industries involved in the wholesale and retail trades, chain enterprises of wholesale and retail trades, large commodity markets with transaction value over 100 million yuan are collected through comprehensive reporting system. Data on enterprises and self-employed individuals below the designated size are collected by sample surveys. However,
(1) The consumption behavior during different human activities is the key factor of basis of MSW generation. Consumer goods, residents, activities could be described as activity subjects, the object, MSW generation media. The three factors corresponding to volume of retail sales of consumer goods, resident groups, and time spent on different activities by residents. (2) Three main factors at quantification were the waste generation per unit of consumer expenditure, consumer expenditure per unit of time spend on different activities, and the ratio of time spent on different activities by different resident groups. The waste generation per unit of consumer expenditure directly represented the quantitative relationship between consumer expenditure and MSW generation. (3) The waste generation in the five provinces (Zhejiang, Guangdong, Hebei, Henan and Sichuan) for the three activities have fluctuated slightly each year from 2003 to 2008. MSW generation of SA remained constant or decreased slightly, while that of LA remained constant or increased slightly in the five provinces. Urban residents in the five provinces all produced the highest waste in MA in 2008, accounting for P70% of total MSW generation. The percentage waste in SA between the five provinces had little variation (approximately 10%), compared to that of LA which had more obvious change (from 10% to 16%). (4) For total MSW generation and MSW generation in MA, the daily MSW generation by females is greater than that by males, and the daily MSW generation on weekends is far greater than that on weekdays. For MSW generation in SA, the daily MSW generation by males is greater than that by females and the MSW generation on weekdays is greater than that on weekends. For MSW generation in LA, the daily MSW generation by males is greater than that by females and the generation on weekends is greater than that on weekdays. (5) The parameter I of the waste generation per unit of consumer expenditure is inversely proportional to per-capita disposable income of urban residents. A linear model describes the relationship between capital GDP and waste generation in SA with a high coefficient of determination.
Acknowledgment This study is supported by National Science & Technology Pillar Program of China (2011BAJ07B04). The authors also thank the support of Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation. References Bartl, A., 2014. Moving from recycling to waste prevention: A review of barriers and enables. Waste Manage. Res. 32 (9), 3–18. Beigl, P., Sandra, L., Stefan, S., 2008. Modelling municipal solid waste generation: A review. Waste Manage. 28, 200–214.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
H.-z. Fu et al. / Waste Management xxx (2015) xxx–xxx Benítez, S.O., Olvera, G.L., Morelos, R.A., Vega, C.A., 2008. Mathematical modeling to predict residential solid waste generation. Waste Manage. 28, S7–S13. Bhat, C.R., Koppelman, F.S., 1993. A conceptual framework of individual activity program generation. Transport. Res. Part A: Pol. Pract. 27, 433–446. Boldrin, A., Christensen, T.H., 2010. Seasonal generation and composition of garden waste in Aarhus (Denmark). Waste Manage. 30, 551–557. Bortoleto, A.P., Kurisu, K.H., Hanaki, K., 2012. Model development for household waste prevention behaviour. Waste Manage. 32 (12), 2195–2207. Cecere, G., Mancinelli, S., Mazzanti, M., 2014. Waste prevention and social preferences: The role of intrinsic and extrinsic motivations. Ecol. Econ. 107, 163–176. Chang, N.B., Lin, Y.T., 1997. An analysis of recycling impacts on solid waste generation by time series intervention modeling. Resour. Conserv. Recycl. 17, 165–186. Chen, H.W., Chang, N.B., 2000. Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resour. Conserv. Recycl. 17 (3 29), 1–18. Cox, J., Giorgi, S., Sharp, V., Strange, K., Wilson, D.C., Blakey, N., 2010. Household waste prevention - a review of evidence. Waste Manage. Res. 28 (3), 193–219. Dennison, G.J., Dodd, V.A., Whelan, B., 1996a. A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland, I. Waste Compos. Resour. Conserv. Recycl. 17, 227–244. Dennison, G.J., Dodd, V.A., Whelan, B., 1996b. A socio-economic based survey of household waste characteristics in the city of Dublin, Ireland, II. Resour. Conserv. Recycl. 17, 245–257. Dyson, B., Chang, N.B., 2005. Forecasting municipal solid waste generation in a fastgrowing urban region with system dynamics modeling. Waste Manage. 25, 669–679. Fu, H.Z., Ho, Y.S., Sui, Y.M., Li, Z.S., 2010. A bibliometric analysis of solid waste research during the period 1993–2008. Waste Manage. 30, 2410–2417. Gay, A.E., Beam, T.G., Brian, W.M., 1993. Cost-effective solid-waste characterization methodology. J. Environ. Eng. 119, 631–644. Gregson, N., Crang, M., Laws, J., Fleetwood, T., Holmes, H., 2013. Moving up the waste hierarchy: Car boot sales, reuse exchange and the challenges of consumer culture to waste prevention. Resour. Conserv. Recycl. 77, 97–107. Hockett, D., Lober, D.J., Pilgrim, K., 1995. Determinants of per capita municipal solid waste generation in the Southeastern United States. J. Environ. Manage. 45, 205–217. Ibiebele, D.D., 1986. Rapid method for estimating solid wastes generation rate in developing countries. Waste Manage. Res. 4, 361–365. Jara-Diaz, S., 2003. On the goods-activities technical relations in the time allocation theory. Transportation 30, 245–260. Joosten, L.A.J., Hekkert, M.P., Worrell, E., 2000. Assessment of the plastic flows in the Netherlands using STREAMS. Resour. Conserv. Recycl. 30, 135–161.
9
Kurisu, K.H., Bortoleto, A.P., 2011. Comparison of waste prevention behaviors among three Japanese megacity regions in the context of local measures and socio-demographics. Waste Manage. 31, 1441–1449. Leao, S., Bishop, I., Evans, D., 2001. Assessing the demand of solid waste disposal in urban region by urban dynamics modeling in a GIS environment. Resour. Conserv. Recycl. 33, 289–313. Li, Z.S., Fu, H.Z., Qu, X.Y., 2011. Estimating municipal solid waste generation by different activities and various resident groups: A case study of Beijing. Sci. Total Environ. 409, 4406–4414. Louis, G.E., 2004. A historical context of municipal solid waste management in the United States. Waste Manage. Res. 22, 306–322. Mazars, 2003. Dublin City Council Waste Management Plan Review Report, Mazars Ltd., Dublin, Ireland. Moriwaki, H., Kitajima, S., Katahira, K., 2009. Waste on the roadside, ‘poi-sute’ waste: Its distribution and elution potential of pollutants into environment. Waste Manage. 29, 1192–1197. National Bureau of Statistics of China., 2008. China Time Use Survey Report 2008, National Bureau of Statistics. (in Chinese). . Neset, T.S.S., Bader, H.P., Scheidegger, R., 2006. Food consumption and nutrient flows – Nitrogen in Sweden since the 1870s. J. Ind. Ecol. 10, 61–75. OECD, 2002. Working Group on Waste Prevention and Recycling, Working Group on Environmental Information and Outlooks: OECD Workshop on Waste Prevention: Toward Performance Indicators 8–10 October 2001, Environment Policy Committee OECD Headquarters, Paris, pp. 1–237. Parfitt, J.P., Lovett, A.A., Sünnenberg, G., 2001. Classification of local authority waste collection and recycling strategies in England and Wales. Resour. Conserv. Recycl. 32, 239–257. Patel, M.K., Jochem, E., Radgen, P., Worrell, E., 1998. Plastic streams in Germany - an analysis of production, consumption and waste generation. Resour. Conserv. Recycl. 24, 191–215. Purcell, M., Magette, W.L., 2009. Prediction of household and commercial BMW generation according to socio-economic and other factors for the Dublin region. Waste Manage. 29, 1237–1250. Thøgersen, J., 1996. Wasteful food consumption: trends in food and packaging waste. Scand. J. Manag. 12, 291–304. Tonglet, M., Phillips, P.S., Bates, M.P., 2004. Determining the drivers for householder pro-environmental behaviour: waste minimisation compared to recycling. Resour. Conserv. Recycl. 42, 27–48. Yenice, M.K., Dogruparmak, S.C., Durmusoglu, E., Ozbay, B., Oz, H.O., 2011. Solid waste characterization of Kocaeli. Pol. J. Environ. Stud. 20, 479–484.
Please cite this article in press as: Fu, H.-z., et al. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.03.029
