Relationship between Consumption patterns and Waste Composition

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Relationship between Consumption patterns and Waste Composition

C h u n s h e n g G u o

Master of Science Thesis Stockholm 2009

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Master of Science Thesis

STOCKHOLM 2009

Relationship between Consumption patterns and Waste Composition

PRESENTED AT

INDUSTRIAL ECOLOGY

ROYAL INSTITUTE OF TECHNOLOGY

Supervisor & Examiner:

Monika Olsson

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TRITA-IM 2009:11 ISSN 1402-7615

Industrial Ecology,

Royal Institute of Technology www.ima.kth.se

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Abstract

The purpose of this study is to explore whether changes in consumption patterns contributed to the changes in waste composition in Jinan during 1999-2008 and to predict trend of the waste composition relevant total household consumer expenditure in the future 10 years. The results reveal that household consumption is the most significant contributors in changes of waste composition.

Although this study points to the possibility of predictions for several important fraction such as food scraps, metal, glass, paper and plastic by according to household consumption, these predictions has not been strong enough to decrease errors, a trend can only be given in the future 10 years.

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List of table and figure

Table 5- 1 FCE of household in Jinan, 1999-2008. ... 14

Table 5- 2 Changes in durable goods per100 household in Jinan, 1999-2008.. ... 14

Table 6- 1 Prediction for TCE, 2009-2018. ... 20

Table 6- 2 Prediction for total waste generated, 2009-2018. ... 21

Table 6- 3 Prediction for food scraps generation, 2009-2018. ... 21

Table 6- 4 Prediction for metal generation, 2009-2018... 21

Table 6- 5 Prediction for glass generation, 2009-2018. ... 22

Table 6- 6 Prediction for papers generation, 2009-2018... 22

Table 6- 7 prediction for plastic generation, 2009-2018. ... 22

Table 6- 8 Share of food scraps, metal, glass, paper and plastic, 2009-2018. .... 23

Table appendix 5- 1 Accuracy grade and critical value. ... 42

Table appendix 5- 2 Notations. ... 43

Table appendix 6- 1 TCE of household per year in Jinan. ... 44

Table appendix 6- 2 Once accumulation for TCE. ... 45

Table appendix 6- 3 Reducing value for TCE. ... 45

Table appendix 6- 4 Once accumulation contrasts for TCE. ... 45

Table appendix 6- 5 Contrast between original date and simulated data for TCE. ... 45

Table appendix 6- 6 Error analysis between raw data and analog data for TCE. ... 46

Table appendix 6- 7 Prediction for TCE, 2009-2018. ... 46

Table appendix 7- 1 Waste output per year in Jinan. ... 47

Table appendix 7- 2 Once accumulation for waste generation... 47

Table appendix 7- 3 Reducing value for waste generation. ... 48

Table appendix 7- 4 Once accumulation contrast for waste generation. ... 48

Table appendix 7- 5 Contrast between original date and simulated data for waste generation. ... 48

Table appendix 7- 6 Error analysis between raw data and analog data for waste generation. ... 49

Table appendix 7- 7 Prediction for waste generated, 2009-2018. ... 49

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Table appendix 8- 1 Food scraps output per year in Jinan. ... 50

Table appendix 8- 2 Once accumulation for food scraps generation. ... 50

Table appendix 8- 3 Reducing value for food scraps generation... 51

Table appendix 8- 4 Once accumulation contrasts for food scraps generation. . 51

Table appendix 8- 5 Contrast between original date and simulated data for food scraps generation. ... 51

Table appendix 8- 6 Error analysis between raw data and analog data for food scraps generation. ... 52

Table appendix 8- 7 Prediction for food scraps generation, 2009-2018. ... 52

Table appendix 9- 1 Metal output per year in Jinan. ... 53

Table appendix 9- 2 Once accumulation for metal generation. ... 53

Table appendix 9- 3 Reducing value for metal generation. ... 54

Table appendix 9- 4 Once accumulation contrasts for metal generation. ... 54

Table appendix 9- 5 Contrast between original date and simulated data for metal generation. ... 54

Table appendix 9- 6 Error analysis between raw data and analog data for metal generation. ... 55

Table appendix 9- 7 Prediction for metal generation, 2009-2018. ... 55

Table appendix 10- 1 Glass output per year in Jinan. ... 56

Table appendix 10- 2 Once accumulation for glass generation. ... 56

Table appendix 10- 3 Reducing value for glass generation. ... 57

Table appendix 10- 4 Once accumulation contrasts for glass generation. ... 57

Table appendix 10- 5 Contrast between original date and simulated data for glass generation. ... 57

Table appendix 10- 6 Error analysis between raw data and analog data for glass generation. ... 58

Table appendix 10- 7 Prediction for glass generation, 2009-2018. ... 58

Table appendix 11- 1 Paper output per year in Jinan. ... 59

Table appendix 11- 2 Once accumulation for paper. ... 59

Table appendix 11- 3 Reducing value for paper. ... 60

Table appendix 11- 4 Once accumulation contrasts for paper. ... 60

Table appendix 11- 5 Contrast between original date and simulated data for paper. ... 60

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Table appendix 11- 6 Error analysis between raw data and analog data for paper.

... 61

Table appendix 11- 7 Prediction for paper generation, 2009-2018. ... 61

Table appendix 12- 1 Plastic output per year in Jinan. ... 62

Table appendix 12- 2 Once accumulation for plastic generation. ... 62

Table appendix 12- 3 Reducing value for plastic generation. ... 63

Table appendix 12- 4 Once accumulation contrasts for plastic generation. ... 63

Table appendix 12- 5 Contrast between original date and simulated data for plastic generation. ... 63

Table appendix 12- 6 Error analysis between raw data and analog data for plastic generation. ... 64

Table appendix 12- 7 Prediction for plastic generation, 2009-2018. ... 64

Fig. 4- 1 Maps of Jinan in Shandong province of China. ... 4

Fig. 4- 2 Waste composition in Jinan, 2008. ... 5

Fig. 4- 3 Scavenger collecting by pedicab. ... 6

Fig. 4- 4 Landfill in Jinan, 2008. ... 7

Fig. 4- 5 Scavenger in landfill. ... 9

Fig. 5- 1 the growth rate of income and consumption expenditure in China, 1999–2008. ... 11

Fig. 5- 3 Changes in consumption expenditure per household in Jinan, 1999–2008. ... 12

Fig. 5- 4 Changes in waste composition in Jinan, 1999–2008. ... 15

Fig. 5- 7 Food consumption expenditure composition of household on the different level of income in Jinan, 2008. ... 17

Fig. 5- 8 Food consumption expenditure of household on the different level of income in Jinan, 2008. ... 18

Fig. 5- 9 Waste composition in sample areas, which are different levels of income in Jinan, 2008. ... 19

Fig. 5- 10 several fractions generated in sample areas, which are different levels of income in Jinan, 2008. ... 19

Fig. 6- 1 Relationship between TCE and food scraps generation. ... 24

Fig. 6- 2 Relationship between TCE and metal generation. ... 24

Fig. 6- 3 Relationship between TCE and glass generation ... 25

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Fig. 6- 4 Relationship between TCE and paper generation. ... 25 Fig. 6- 5 Relationship between TCE and plastic generation. ... 26

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Glossary of terms and Abbreviations

Consumption Pattern: The combination of qualities, quantities, acts and tendencies characterizing a community or human group's use of resources for survival, comfort and enjoyment.

Household Consumer Expenditure: Average annual expenditures per consumer unit, which is similar to a household.

Food Scrap: it is readily biodegradable in biological treatment such as composting or anaerobic digestion.

Municipal Solid Waste: refers to municipal mixed type of solid waste generated by household, business, traditional market, and street.

Disposal: final placement or destruction of waste.

Composting: it is process of producing compost through aerobic decomposition of biodegradable organic matter.

Incineration: waste treatment technology that involves the combustion of waste at high temperature, generally accompanied by energy recovery.

Landfill: controlled site for depositing waste that it not intended to be moved.

Recycling: it is means use by one producer of a waste generated by another producer, or reuse as a material component within an existing manufacture process.

Waste collection: component of waste management that results in the passage of a waste material from the source of production to the point of temporary location, treatment or final disposal.

Waste transportation: movement of waste from one place to another.

RENMINBI: Chinese currency TCE: Total consumer expenditure FCE: Food Consumer Expenditure DCE: Dress Consumer Expenditure GDP: Gross Domestic Product RMB: RENMINBI

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1. Introduction

Consumption pattern as a functional element plays important role in the economic growth process. In fact, consumption is the largest component of GDP in many countries—especially in developed countries.

The problem posed by the increasing importance of consumption to the economy and society is that the consumer is becoming an influential force pushing goods to be more waste resulting in increased environmental pressure. Therefore, the control of solid waste pollution is an important aspect of environmental protection in a country.

The recent Jinan’s economy grew faster. This rapid growth increased Jinan citizens’

incomes, allowing them to escape from the poverty of the 1990s and to focus on improving their quality of life. Although total rising incomes might not spend on goods, also a part of that is spending on tax and service, but crucial issue that consumption pattern reflect change of waste composition already become certain focus, more and more scholars begin to pay attention to it.

Not surprisingly, there is a little research about the impact of consumption on the changes of waste composition. However, given that rising consumption demands the continuous expansion of waste generation, more systematic attention is needed concerning the impacts of consumption patterns on the MSW.

The purpose of this study is to explore whether changes in consumption patterns contribute to the changes in waste composition, then predict total consumer expenditure (TCE), total waste generation and several important fraction by grey model respectively, then establish a relationship model to represent relationship between TCE and share of several important fraction. Furthermore, the findings would be helped waste management department to batter plan.

2. Aim and Objective:

Analyze waste composition and consumption pattern in Jinan city in the latest 10 years, in order to identify relationship between waste composition and consumption pattern and predict how it will change in the future.

The aims of this thesis will be realized through achieving the following objectives:

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 Identify the state of municipal solid waste and waste management in Jinan;

 Indentify Jinan’s state of household consumer expenditure in the latest 10 years;

 Analyze changes of consumption patterns of household in Jinan in the latest 10 years;

 Analyze changes of municipal solid waste composition in Jinan in the latest 10 years;

 Analyze household consumption patterns with groups of different income levels in 2008;

 Analyze waste composition for groups of different income levels in 2008;

 Prediction of the total consumer expenditure (TCE), total waste generated, food scraps generation, metal generation, glass generation, paper generation and plastic generation in the future 10 years;

 Identify the relationship between TCE and several important waste fractions( food scraps, metal, glass, paper and plastic), and establish a model that express the relationship between TCE and several waste fractions;

 Represent trends of several waste fractions connected to changes of TCE in the future 10 years in Jinan.

3. Methodology

There were several things that had to be determined and taken into consideration in the beginning of the work. To start with, I had to determine in what way I would give out the relationship between waste composition and consumption pattern by a research method. Secondly, it was important to define the target group and could take that into consideration. Sources used in this thesis to generate the project’s conclusions were: a literature review in the relevant fields, searches in databases from waste management organization and on the web, a field trip to Jinan, interviews with experts and researches, and two models. One model is Grey model, which is a model for forecasting, in this thesis use its simple form GM(1,1), because GM(1,1) is better to reduce errors and it

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only take into account one series time data like total waste generation, don’t take into account other factors such as population, lifestyle etc. Another one is Relationship model, which correlated available data for total consumer expenditure (TCE) and several fractions, final output of model is a set of equations. They will be explained in chapter 6.

System boundary: In this thesis, all data about consumption form Jinan Statistic Bureau and all data about MSW form Jinan City Appearance & Environmental Sanitation Administration Bureau. The analysis in chapter 5 or the calculations in chapter 6 are based on the above data.

4. The Study Area

Jinan locates in the center of Shandong province, and it is political, economical and cultural center of Shandong province; during the recent years, there has been great economic growth in Jinan, particularly in latest 5 years, the annual average GDP growth rate was more than 15%.

4.1 General information about Jinan

Jinan is the capital of Shandong Province, on China's east coast (see Fig 4-1). Jinan locates in the north-western part of Shandong province at 36° 40′ northern latitude and 116° 57′ east of Greenwich. Location falls within the warm temperate continental monsoon climate zone due to Jinan’s geographical, Jinan has four distinct seasons. The city is dry and rainless in spring, hot and rainy in summer, crisp in autumn and dry and cold in winter. The average annual temperature is 14.2°C, and the annual rainfall is around 675 mm.

The sub-provincial city of Jinan administers 10 county-level divisions, including 6 districts (licheng District, lixia District, Shizhong District, Huaiying District, Tianqiao District, and Changqing District), 1 county-level city (Zhangqiu City) and 3 counties (Pingyin County, Jiyang County and Sanghe County). Total area is 8,177 km².

Jinan's 2008 estimated population is 6.03 million in the whole city-jurisdiction area, with a total of 3.38 million living in urban areas. Jinan’s estimated GDP is 301.7 billion, which has an increase of 13% compared with last year (Jinan Statistic Bureau, 2008).

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Fig. 4- 1 maps of Jinan in Shandong province of China.

4.2 Current state of MSW in Jinan

Jinan solid waste management apartment is Jinan City Appearance & Environmental Sanitation Administration Bureau, its management level is still relative low.

Following shows state of MSW in Jinan:

4.2.1 Solid waste generation

Jinan’s major generation source of municipal solid waste is from households. This waste consists mainly of food scraps, yard waste and wrapping materials. It is a mixture of other organic and non-organic, recyclable and non-recyclable waste, and even hazardous and non-hazardous materials. The other sources are from traditional markets, commercial areas, and street wastes. The total amount of produced solid waste from those sources in the area of Jinan is about 2,300 tons per day (Jinan City Appearance &

Environmental Sanitation Administration Bureau, 2008).

4.2.2 Solid waste composition

The amount of solid waste is dominated by the organic fraction (52%, 2008) that mainly comes from food scraps (see Fig 4-2). This fraction contributes to about 60 – 70% of the water content of the Jinan’s solid waste in summer and fall, about 30-40% in

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spring and winter. The average Lower heat value of fraction is about 4500kJ/kg in Jinan.

The consumption pattern, materials utilization and climate have been identified as the key factors for the characteristics of this waste fraction (Jinan City Appearance&

Environmental Sanitation Administration Bureau, 2008).

52%

0.4% 1%

5% 8%

2.5% 0.5%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

waste composition

food scraps metal glass paper palstic textile wood

Fig. 4- 2 Waste composition in Jinan, 2008. Source: appendix 3, Jinan city Appearance &

Environmental Sanitation Administration Bureau Statistical Bureau, 2008.

4.2.3 Collection and transportation system

In most developed countries, solid waste is collected from urban areas by compactor trucks, which collect waste from each household once or twice a week. However, there are several reasons results in the collection system does not work in the developing urban regions. Firstly, truck often difficultly accessible to individual household due to not suitable read conditions. Secondly, compacting waste is unfeasible and frequent result in equipment failure due to the waste in poorer areas is denser and more corrosive by reason of a high organic content especially in summer. It is partly resulted from these two conditions; the costs are very high, so it is difficultly implement due to People’s ability and willingness to pay for the services are low in developing regions.

Thirdly, since weak local authorities and the lack of precedent for paying fees like financing cycle of the difficult for recover the cost of collection services. These difficulties have prompted the development of new collection systems better suited to

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developing urban areas. Three different types of systems are presented here:

house-to-house collection, communal collection and block collection. There are different for the equipment necessary (transport and storage), the effort required of households, and cost.

House-to-house collection. There is several house-to-house collection systems designed to be appropriate to Jinan. These programs are significantly different from traditional developing country’s collection systems in financing, organization, and technology. A suitable program for house-to-house collection is collecting in the local waste management committee and a residential area in Jinan. This program used indigenously designed and produced pickup for collection. In addition, other some programs developed new ways of getting households to pay for house-to-house. For example, a primary collection was used by scavenges collecting, in the primary collection process scavenges are responsible for collecting their own fees.

Fig. 4- 3 Scavenger collecting by pedicab.

Communal Collection Sites. There are alternative methods of collection for communal collection sites in Jinan. Sometimes these programs are consisted of several layers of collection networks. One program used small pickup truck to transport

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communal drums to truck at collection points in suburb of Jinan. The trucks are then periodically emptied by the Jinan City Appearance & Environmental Sanitation Administration Bureau. These programs, which encourage recycling by paying different prices for different materials, can be utilized financial.

Block Collection. Block collection has been implemented in several areas in Jinan. In this system, a collection vehicle is traveled a scheduled route, stopping periodically for residents to bring their refuse. Block collection has an advantage that can be eliminated the need for intermediate storages equipments, which increase cost of collection. However, there has been negative experience with block collection. Because this free service was more popular than a community-run collection service of which a fee was charged, so after a period of time, residents were willing to carry their rubbish including recycling material like glass, pop can etc, to the trucks.

4.2.4 Disposals of MSW

Landfill

Before 2009 year, there is only one landfill owned and operated by the municipality which receives almost all collected waste in Jinan today, the second landfill with a capacity of about 20 million m² will put into use in October, 2009. The first landfill is located in Jiyang country, which began to use it in 1998 and it has built out a landfill of 53, 000 m² (primary is 1.14 million m²).

Fig. 4- 4 Landfill in Jinan, 2008.

Incineration

Another method of treating solid waste in Jinan is incineration. There is one small-scale

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incinerator in operation next to the landfill with a capacity of about 250 ton/day and operation 6 hours per day. Therefore this system is only able to handle about 0.1% of the total generated waste per day. Furthermore, they are not operated on a daily basis, and technical information is deficient, such as a detailed description of the system operation, the treatment process, and how these incinerators have performed so far.

And one large scale incinerator will be built with a capacity of about 2300 ton/day, which is 92% of the total generated waste per day in Jinan.

Composting

The composting of organic solid waste has been introduced as part of a waste minimization program set up by the municipality. The idea is to reduce the waste quantity going to landfill. Basically, the composting of organic wastes is a bio-oxidative process involving the mineralization and partial humidification of the organic matter, leading to a stabilized final product, free of phytotoxicity and pathogens and with certain humic properties. During the first phase of the process, the simple organic carbon compounds are easily mineralised and metabolized by the microorganisms, producing CO2, NH3, H2O, organic acids and heat. The accumulation of this heat raises the temperature of the pile. Composting is a spontaneous biological decomposition process of organic materials in a predominantly aerobic environment.

During the process, bacteria, fungi and other microorganisms including micro arthropods, break down organic materials to the stable, usable organic substances called compost. The composting also implies the volume reduction of the wastes, the destruction of weed seeds and of pathogenic microorganisms. There was big composting system which was able to handle about 1000 tons waste, which was about 40% of the total waste per day, but it already, has stopped due to some technical problems.

Recycling

The recycling of municipal solid wastes in Jinan relies largely on the informal recovery of materials from waste carried out by human scavengers, which involves thousands of scavengers, collectors, and waste suppliers. Scavengers recover materials to sell for reuse or recycling, as well as diverse items for their own consumption. There are several types of scavengers as follows:

 Collection crews sort recyclables while on their collection routes. Generally, the collection crews activate in open collection vehicles that offer easy access for the

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recovery of recyclables from collected mixed wastes. In addition, when compactor trucks use, sorting of recyclable materials also exists before the compacting of the refuse (Martin Medina, 2000).

 Itinerant buyers purchase source-separated recyclables from residents. In Jinan itinerant buyers purchase from residents various types of items for reuse and recycling, such as cans, bottles, paper, and old durable goods. The vehicles used to carry these materials include pushcarts, pedicabs and small pickup trucks (Martin Medina, 2000).

 Scavengers retrieve materials at the communal storage sites, as well as from commercial and residential containers placed curbside. Because generally wealthy individuals are toward to discard more recyclables and items that can be repaired or reused, so scavengers often activate in high-income residential areas, hotels and stores (Martin Medina, 2000).

 On the streets or public spaces, picking up litter. In Jinan, much scavenges in the city recover recyclables from garbage thrown into the streets (Martin Medina, 2000).

 At landfills. Before the wastes are landfill, scavengers recover materials. About 200 scavenges have been integrated into the landfill per day in Jinan (see Fig 4-5).

As soon as the refuse is dumped on the ground, scavengers collect recycling material from the mixed wastes. Later during the day, bulldozers compact the wastes and cover them with a layer of earth (Martin Medina, 2000).

Fig. 4- 5 Scavenger in landfill.

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Based on the above description of waste disposal, it’s easy to know that landfill is responsible for more than 90%, incineration accounts for about 1%, and materials recycling cover less than 20% of the total waste.

5. Changes of consumption patterns and waste composition

In this chapter, first of all, state of household consumption of Jinan and changes of consumption patterns in latest 10 years were discussed. Second of all, changes in waste composition were represented over the period. In addition, consumption pattern and waste composition in three districts on different income level were discussed.

5.1. Income growth and changes in household consumption

During the last decade, Jinan has undergone a remarkable transformation from a general city to a new large city of China. The annual GDP growth rate averaged 14%

between 1999 and 2008, from 88.12 billion Yuan in 1999 to 301.74 billion Yuan in 2008 (Jinan Statistic Bureau, 1999-2008). In general, there is an obvious link between consumption and income, and the consumption patterns tend to change with the increasing income.

Here, I review the trends in household income and consumption expenditures first and then analyze the observed changes. As shown in Fig. 5-1, over the period of 1999–2008, urban households achieved steady gains in real income and were able to increase their consumption expenditures. The growth rate for income and TCEs of urban households, respectively, were four, two times as high as the growth rate of GDP (Jinan city Statistical Bureau, form 1999 to 2008).

Secondly, TCE have a similar growth with the income, total consumer expenditure (TCE) lag behind income growth, as seen in Fig. 5-1. This fact can be partly explained by the lifestyle approach, which indicates that the consumption expenditures tend to rise in urban settings (Uusitalo, 1986).

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5.00%

10.00%

15.00%

20.00%

25.00%

growth rate of GDP

growth rate of income for household

growth rate of total consumer expenditure for household

Fig. 5- 1 the growth rate of income and consumption expenditure in China, 1999–2008. Source:

appendix 1, Jinan city Statistical Bureau of Jinan (from 1999 to 2008). Note: current price. The trends in income and consumption expenditure here correspond to the case of urban households without urban farmer.

5.2. Changes of consumption patterns in latest 10 years

5.2.1 Changes of consumption on the whole

As income levels increased, the capital living expenditure composition of urban households grew at an average annual rate of 12.8%, from 7163 Yuan in 1999 to 20802 Yuan in 2008 (Jinan city Statistical Bureau, from 1999 to 2008 ). With this quantitative rise in spending came a shift in the type of goods and services under demand. For example, between 1999 and 2008, expenditures on food decreased from 34.4 to 31.3 % of total household expenditures, the share of education and culture expenses and miscellaneous expenses fell from 15.5 to 12.5% and from 5.5 to 3 % respectively. In addition, the share of transportation and communication expenses as well as habitation expenses increased from 6.7 to 17% and from 8.5 to 10.6%, respectively. Furthermore, household facilities, articles expenses and healthcare expenses growth appeared fluctuated, but the general trend of household facilities, articles expenses shows a decrease and overall trend of healthcare expenses and increase (see Fig. 5-2 and Fig.

5-3).

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12 0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Fig. 5- 2 Changes in consumption expenditure per household in Jinan, 1999–2008. Source:

appendix 1, Jinan city Statistical Bureau, form 1999 to 2008.

0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 14000.00

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Miscellaneous Commodities expenses

habitation expenses

education and culture expenses

transportation and communications expenses healthcare expenses

Household Facilities, Articles expenses

dress expenses

Fig. 5- 3 Changes in consumption expenditure (RMB) per household in Jinan, 1999–2008.

Source: appendix 1, Jinan city Statistical Bureau, form 1999 to 2008. Note: at current Price.

Otherwise, changes of consumption patterns reflected changes in consumption of durable goods, shown in table 1. On the one hand, the important durable consumer goods in traditional consumption patterns such as color TV, refrigerators, mobile phone and washing machine have already reached saturation. On the other hand, microwave ovens, smoke discharged machine, Vacuum cleaners, air-condition, computer and automobile, which have improved the quality of household life, however, increased in

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different rate of growth.

The above mention has pointed out that consumption patterns were changing form traditional patterns to modern patterns, that is manifested in the following two aspects:

First of all, it has been observed that household discretionary spending increases as incomes rise. This general trend is reflected in the statistics tracking consumption expenditures in Jinan during 1999-2008. As shown in figure 5-3 and table 5-1, the increased spending on service, drink and dining out indicates that household consumption is becoming more discretionary-oriented. The increase in the share of household expenditures for dining out is particularly conspicuous. In 1999 households spend 6.8% of total consumption expenditures eating food away from home; this item accounted for 8.2% of total consumption expenditures over 10 years. That is annually household expenditures on dining out grew at an average annual rate of 11.5%, from 436 Yuan in 1999 to 1144 Yuan in 2008. In addition, the share of expenditures on non-discretionary items such as food consumed at home, along with dress consumer expenditure (DCE) and habitation consumer expenditure diminished steadily.

Secondly, another important indicator is changes in food patterns of Jinan’s households.

On the one hand, the composition of the habitant food was changed. Household more spend on vegetable, dry and fresh fruits and drink. Expenditures on meat and cereals, on the other hand, fell from 6.3% of total household consumption in 1999 to less than 4.4%

in 2008 and from 3.1% in 1999 to 2.1% in 2008, respectively. Thus it is questionable whether cereals and meat remains the primary staple of the Jinan’s household diet. On the other hand, the largest share of food expenditure went to dining out in 2008. It very enhanced food consumer expenditure. These basic changes indicate a larger transformation of consumption patterns towards more discretionary-oriented items.

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Selector 1999 2002 2005 2007 2008

Food consummer expenditure(%)

34.4% 34.6% 33.0% 31.5% 31.3%

Cereals(%) 3.1% 2.9% 2.5% 2.1% 2.1%

Oil(%) 1.1% 1.1% 1.0% 0.9% 0.8%

Meat and Poultry(%) 6.3% 6.0% 6.5% 4.5% 4.4%

Eggs(%) 1.1% 1.2% 1.1% 0.8% 0.8%

Aquatic Products(%) 2.1% 1.6% 1.6% 1.7% 1.7%

Vegetable(%) 2.6% 2.6% 2.8% 2.8% 2.9%

Saccharide(%) 0.3% 0.3% 0.3% 0.3% 0.2%

Drink(%) 1.5% 1.7% 2.1% 2.4% 2.4%

Dry and Fresh Fruits(%) 2.1% 2.4% 2.9% 2.9% 3.0%

Cake(%) 1.0% 0.9% 0.9% 0.7% 0.7%

Milk(%) 2.1% 2.1% 2.2% 2.1% 2.0%

Dining out(%) 6.8% 6.9% 7.7% 8.6% 8.2%

Food consummer expenditure of urban household in major years

Table 5- 1 Food consumer expenditure of household in Jinan, 1999-2008. Source: appendix 1, Jinan city Statistical Bureau, form 1999 to 2008.Note: % means percentage of total consumer expenditure

Type 1999 2000 2002 2004 2005 2006 2007 2008

Color TV(number) 123 132 123 126 127 122 121 113

Refrigerators(number) 99 99 94 97 97 97 99 93

Washing machine(number) 48 100 92 99 97 99 95 88

Microwave ovens(number) 22 32 37 50 53 58 62 67

Smoke discharged machine(number) 76 84 70 82 87 88 91 93

Vacuum cleaners(number) 22 19 13 17 18 16 18 18

Air-condition(number) 48 65 73 96 104 112 113 97

Computer(number) 9 20 32 49 54 64 72 61

Mobile phone(number) 15 29 72 130 145 163 176 145

Automobile(number) 2 3.67 5.4 6.8 9.3 15.2

Table 5- 2 Changes in durable goods per100 households in Jinan, 1999-2008. Source:

Jinan city Statistical Bureau, form 1999 to 2008.

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5.3 Changes of waste composition in latest 10 years

The composition of MSW is extremely various in the city. Fig. 5-4 shows the changes of waste composition in Jinan city.

As it can be seen from the bar graph, with only the data of 10 years, it appears to be significant trends in the composition of Jinan waste stream. The composition of street cleaning percent (mainly include dust) has declined by 15.4 points over the period recorded. Food scraps make up the largest component of MSW in the city. Based on the 2008 waste composition study, approximately 413,790 tons or 52.8 percent of the MSW waste stream in 2008 was food scraps, and it appeared to be a rapid growth with an average increase of 3% per year, food scraps increase from 41.9% to 52.8% of the total waste stream. In addition, other fractions have increased, although in some certain years, they declined several points. At the same time, the plastic increased steadily. (As see Fig. 5-4).

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

food scraps

street cleaning

metal glass paper plastic textile wood

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Fig. 5- 4 Changes in waste composition in Jinan, 1999–2008. Source: appendix 3, Jinan city Appearance & Environmental Sanitation Administration Bureau, forms 1999 to 2008.

5.4 Changes of consumption patterns on the different level of income

The study was based on the income group or family budget and the data were collected from the three selected different areas of Jinan namely downtown, Huaiyin district and

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Changqing district. Downtown, Huaiyin district and Changqing district were at high, middle and low income level respectively. These three areas were generally considered as the main representative units of the whole Jinan consumption pattern situation. The data were randomly collected through Jinan Statistic Bureau investigating in 2008 (see appendix 2). In this study, a total of 240 households were selected.

Seen from Fig. 5-5, on the one hand, the three income categories have very different consumption patterns. The proportions of consumer expenditure composition of high income group more than other two income groups along with spending on food and miscellaneous commodities, but still high income group spent more on miscellaneous communities than the low income group (see Fig. 5-5); otherwise amount of consumer expenditure on each items of high income group more than other group (see Fig. 5-6).

The contrast in the share of difference income households’ expenditures for dress and food are particularly conspicuous, whether on proportions or amount of (see Fig. 5-5, 6-6). On the other hand, the shares of consumer expenditure on food were 39.4%, 30.6% and 27.1 respectively, it decreased with the rising income, but the amount of consumer expenditure on food increase with the rising income; in that spending on dining out, which take largest proportion in food consumer expenditure (FCE), represented different trends like dress, house hold facilities, healthcare, transportation, education and habitation (see Fig. 5-7, 5-8).

Fig. 5- 5 Consumption expenditure composition of household on the different level of income in Jinan, 2008. Source: appendix 2, Jinan city Statistical Bureau, 2008. Note: % means percentage of total consumer expenditure

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Fig. 5- 6 Consumption expenditure (RMB) of household on the different level of income in Jinan, 2008. Source: apendix2, Jinan city Statistical Bureau, 2008.

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

Changqing district(low income) Huaiyin district(middle income) downtown(hig h income)

Fig. 5- 7 Food consumption expenditure composition of household on the different level of income in Jinan, 2008. Source: appendix 2, Jinan city Statistical Bureau, 2008. Note: % means percentage of total consumer expenditure.

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18 0

200 400 600 800 1000 1200 1400 1600 1800 2000

Changqing district(low income) Huaiyin district(middle income) downtown(high income)

Fig. 5- 8 Food consumption expenditure (RMB) of household on the different level of income in Jinan, 2008. Source: appendix 2, Jinan city Statistical Bureau, 2008.

5.5 Changes of waste composition on the different level of income

The study was based on the income group or family budget and data were collected from the three selected different areas of Jinan namely downtown, Huaiyin district and Changqing district. The data was collected through Jinan City Appearance &

Environmental Sanitation Administration Bureau in 2008. The data shows an average value for each district in 2008.

As can be seen from Fig. 5-9, on the one hand, there are two conspicuous trends, first, the share of street cleaning generated and wood fell with income rising, for example, in Changqing district, Huaiying district and downtown, share of street cleaning generated were 41.5%, 23.6% and 11.4% respectively. Secondly, the share of food scraps, metal, glass, paper and plastic increased with the rising income, and share of paper and plastic is particularly conspicuous. The composition in the three different districts accord to Fig 5-10, but it should be taking into account only represent a small of the inhabitant.

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19 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Changqing distrct Huaiyin district downtown

wood textel palastc paper glass metal

street cleaning food scrap

Fig. 5- 9 Waste composition in sample areas, which are different levels of income in Jinan, 2008.

Source: appendix 4Jinan city Appearance & Environmental Sanitation Administration Bureau, 2008.

Fig. 5- 10 several fractions generated in sample areas, which are different levels of income in Jinan, 2008. Source: appendix 4, Jinan city Appearance & Environmental Sanitation Administration Bureau, 2008.

6. Relationship between consumption patterns and waste composition

In this chapter two models were used. On the one hand, a description about how to use Grey Model GM(1,1) was represented and then total consumer expenditure, annual

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MSW generation, and annual several important fractions (food scraps, metal, glass, paper and plastic) were predicted in the future 10 years by GM(1,1); on the other hand, a relationship model was established and then relationships between total consumer expenditure ( RMB) and the share of several important fractions (%) were showed.

6.1 Prediction for changes of consumption and waste generated in future 10 years

The Grey Model GM(1,1) is a time series forecasting model. It has three basic operations: (1) accumulated generation, (2) inverse accumulated generation and (3) grey modeling. Thereinto accumulated generation means original data gradual accumulated; inverse accumulated generation means inverse accumulate new data to become prediction data for original data; grey modeling means prediction for the future data. The grey forecasting model uses the operations of accumulated to construct differential equations. Intrinsically speaking, it has the characteristics of requiring less data. The detail steps of GM(1,1) was summarized in appendix 5.

Prediction results for total consumer expenditure, annual MSW generation, and annual several important fractions (food scraps, metal, glass, paper and plastic) show as following:

Year Total consumer expenditure per household (RMB/year)

2009 14676

2010 16174

2011 17826

2012 19646

2013 21652

2014 23863

2015 26300

2016 28985

2017 31945

2018 35207

Table 6- 1 Prediction for TCE, 2009-2018.

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Year Annual waste output（t/a) Average day output（t/d)

2009 850176 2362

2010 880560 2446

2011 912024 2533

2012 944604 2624

2013 978336 2718

2014 1013292 2815

2015 1049472 2915

2016 1086984 3019

2017 1125792 3127

2018 1166004 3239

Table 6- 2 Prediction for total waste generated, 2009-2018.

Year Food scraps generated (ton/day)

2009 1182

2010 1250

2011 1322

2012 1397

2013 1478

2014 1563

2015 1652

2016 1747

2017 1848

2018 1954

Table 6- 3 Prediction for food scraps generation, 2009-2018.

Year Metal generated (ton/day)

2009 8

2010 9

2011 11

2012 14

2013 16

2014 19

2015 23

2016 28

2017 33

2018 39

Table 6- 4 Prediction for metal generation, 2009-2018.

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22 Year Glass generated (ton/day)

2009 21

2010 23

2011 25

2012 27

2013 30

2014 33

2015 36

2016 39

2017 42

2018 46

Table 6- 5 Prediction for glass generation, 2009-2018.

Year Paper generated (ton/day)

2009 111

2010 120

2011 128

2012 138

2013 148

2014 159

2015 170

2016 183

2017 196

2018 210

Table 6- 6 Prediction for papers generation, 2009-2018.

Year Plastic genetated (ton/day)

2009 194.93

2010 212.64

2011 231.95

2012 253.03

2013 276.02

2014 301.09

2015 328.45

2016 358.29

2017 390.84

2018 426.35

Table 6- 7 prediction for plastic generation, 2009-2018.

Detailed contents about calculation processes of above results are in appendix 6-12.

According to above results, waste composition in the future 10 years shows in table 6-8.

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23 Type Food scraps

generated (%) Metal(%) Glass(%) Paper(%) Plastic(%)

2009 50.1% 0.1% 0.9% 4.7% 8.3%

2010 51.1% 0.2% 1.0% 4.9% 8.7%

2011 52.2% 0.2% 1.0% 5.1% 9.2%

2012 53.3% 0.2% 1.1% 5.3% 9.6%

2013 54.4% 0.3% 1.1% 5.4% 10.2%

2014 55.5% 0.3% 1.2% 5.6% 10.7%

2015 56.7% 0.4% 1.2% 5.8% 11.3%

2016 57.9% 0.5% 1.3% 6.1% 11.9%

2017 59.1% 0.6% 1.4% 6.3% 12.5%

2018 60.3% 0.7% 1.4% 6.5% 13.2%

Table 6- 8 Share of food scraps, metal, glass, paper and plastic, 2009-2018. Note: % means percentage of total waste generation

6.2 Relationship between consumption patterns and waste composition

Because waste composition’ changes as a direct consequence of human activities, the consumption patterns of a city has been chosen as the first major parameter determining the waste composition.

Relationship model design:

Here the model developed attempts to estimate the composition of municipal solid waste (MSW). Available statistical data for the annual percentage of food scraps, metal, glass, paper and plastic in total MSW generation as well as the TCE (total consumer expenditure) of household annual spending total RMB (Chinese currency) have been correlated. The information used spans the period 1999-2008.

The final output of the model is a set of equations, derived through the best simple power-function fit of the data considered, which facilitate the prediction of the required three parameters of the MSW arising.

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24 0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

0 5000 10000 15000 20000 25000 30000 35000 40000

relationship between total comsumer expenditure and food scraps generated

Fig. 6- 1 Relationship between TCE and food scraps generation.

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

0 5000 10000 15000 20000 25000 30000 35000 40000

relationship between total comsumer expenditure and metal generated

Fig. 6- 2 Relationship between TCE and metal generation.

■1999-2008 raw data

▲2009-2018 predicted data

■1999-2008 raw data

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25 0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

0 5000 10000 15000 20000 25000 30000 35000 40000

relationship between total comsumer expenditure and glass generated

Fig. 6- 3 Relationship between TCE and glass generation

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

0 5000 10000 15000 20000 25000 30000 35000 40000

relationship between total comsumer expenditure and paper generated

Fig. 6- 4 Relationship between TCE and paper generation.

■1999-2008 raw data

Relationship between Consumption patterns and Waste Composition

Relationship between Consumption patterns and Waste Composition

Relationship between Consumption patterns and Waste Composition

INDUSTRIAL ECOLOGY

ROYAL INSTITUTE OF TECHNOLOGY

Abstract

Table of contents

List of table and figure

Glossary of terms and Abbreviations

1. Introduction

2. Aim and Objective:

3. Methodology

4. The Study Area

5. Changes of consumption patterns and waste composition

6. Relationship between consumption patterns and waste composition