relationship between consumption patterns and waste...

Relationship between Consumption patterns

and Waste Composition

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

Master of Science ThesisStockholm 2009

Chunsheng Guo

Master of Science ThesisSTOCKHOLM 2009

Relationship between Consumption patternsand Waste Composition

PRESENTED AT

INDUSTRIAL ECOLOGY ROYAL INSTITUTE OF TECHNOLOGY

Supervisor & Examiner:

Monika Olsson

TRITA-IM 2009:11 ISSN 1402-7615 Industrial Ecology, Royal Institute of Technology www.ima.kth.se

Relationship between Consumption Patterns and Waste Composition

i

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.


ii

Table of contents List of table and figure .................................................................................................. iv

Glossary of terms and Abbreviations ..........................................................................viii

1. Introduction ............................................................................................................ 1

2. Aim and Objective: ................................................................................................ 1

3. Methodology .......................................................................................................... 2

4. The Study Area....................................................................................................... 3

4.1 General information about Jinan ...................................................................... 3

4.2 Current state of MSW in Jinan ......................................................................... 4

4.2.1 Solid waste generation .......................................................................... 4

4.2.2 Solid waste composition ....................................................................... 4

4.2.3 Collection and transportation system .................................................... 5

4.2.4 Disposals of MSW ................................................................................ 7

5. Changes of consumption patterns and waste composition .................................. 10

5.1. Income growth and changes in household consumption .............................. 10

5.2. Changes of consumption patterns in latest 10 years ..................................... 11

5.3 changes of waste composition in latest 10 years ........................................... 15

5.4 Changes of consumption patterns on the different level of income ............... 15

5.5 Changes of waste composition on the different level of income ................... 18

6. Relationship between consumption patterns and waste composition .................. 19

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

.............................................................................................................................. 20

6.2 relationship between consumption patterns and waste composition ............. 23

7. Discussion ............................................................................................................ 26

8. Conclusion ........................................................................................................... 28

9. Acknowledgements .............................................................................................. 29

Reference ..................................................................................................................... 30

Appendix 1 Capital living expenditure composition of urban households .................. 32

Appendix 2 Capital living expenditure of urban households in Changqing, Huaiyin

and downtown in 2008 ................................................................................................. 34

Appendix 3 Waste composition of urban from 1999 to 2008 ...................................... 36

Appendix 4 Waste composition in Changqing, Huaiyin and downtown in 2008 ........ 37


iii

Appendix 5 description about GM(1,1) ....................................................................... 38

Appendix 6 prediction for total consumer expenditure ............................................... 44

Appendix 7 prediction for total waste generation ........................................................ 47

Appendix 8 prediction for food scraps generation ....................................................... 50

Appendix 9 prediction for metal generation ................................................................ 53

Appendix 10 prediction for glass generation ............................................................... 56

Appendix 11 prediction for paper generation .............................................................. 59

Appendix 12 prediction for plastic generation ............................................................. 62


iv

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


v

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


paper. ............................................................................................................ 60


vi

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


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


vii

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

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


viii

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


1

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:


2

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


3

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).

http://en.wikipedia.org/wiki/Shandong

http://en.wikipedia.org/wiki/Degree_(angle)

http://en.wikipedia.org/wiki/Minute_of_arc

http://en.wikipedia.org/wiki/Latitude

http://en.wikipedia.org/wiki/Greenwich

http://en.wikipedia.org/wiki/Monsoon

http://en.wikipedia.org/wiki/Political_divisions_of_China#County_level

http://en.wikipedia.org/wiki/District_of_China

http://en.wikipedia.org/wiki/County-level_city

http://en.wikipedia.org/wiki/County_of_China


4

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


5

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


6

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


7

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 m

2).

Fig. 4- 4 Landfill in Jinan, 2008.

Incineration

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


8

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


9

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.


10

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).


11

0.00%

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).


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

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

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


13

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.


14

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.


15

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


16

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


17

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(high 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.


18

0200400600800

100012001400160018002000

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.


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


20

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.


21

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.


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.


23

TypeFood 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.


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



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


■1999-2008 raw data



26

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

0 5000 10000 15000 20000 25000 30000 35000 40000

relationship between total comsumer expenditure and plastic generated

Fig. 6- 5 Relationship between TCE and plastic generation.

Results of sample analyses of MSW made over period 1999-2008 in Jinan are plotted

against the corresponding values of TCE in Fig. 6-1, 6-2, 6-3, 6-4, 6-5. From the data

presented, the percentage by weight of the individual fractions of the MSW can be

correlated with the TCE via the following polynomial functions:

Food scraps=2×10-12

*TCE3-4×10

-8*TCE

2-0.465

Metal= -3×10-14

*TCE3+5×10

-10*TCE

2-3×10

-6*TCE+0.008

Glass=2×10-14

*TCE3-3×10

-10*TCE

2+2×10

-6*TCE+0.002

Paper= -6×10-13

*TCE3+9×10

-9*TCE

2-6×10

-5*TCE+0.118

Plastic=5×10-13

*TCE3-7×10

-9*TCE

2+6×10

-5*TCE-0.119

The degree of accuracy of this model is determined by the reliability of the published

information, which has been provided by Jinan City Statistical Bureau and Jinan City

Appearance & Environmental Sanitation Administration Bureau. All the waste

fractions that are examined have been correlated with the TCE figures. The selections

of a third degree of polynomial equation, for approximating the plotted data, were

imposed by the need for a consistent approach throughout our analysis. The degree of

the selected polynomial equation provide the best fit curves for the majority of the

waste fraction as can be seen from the relevant figure.

7. Discussion

From the above study and analysis of the consumption patterns and waste composition,

■1999-2008 raw data



27

the recent data collected in Jinan, discussions can be drawn as follows:

The share of food scraps fraction of MSW grew with consumer expenditure rising from

1999 to 2008 and figure 6-1 shows less scatter, which means close relationship between

total household consumer expenditure and food scraps generation. Otherwise, through

the analysis of consumption patterns and waste composition in chapter 5, the share of

food scraps generation increased, contrarily share of households food consumer

expenditure (FCE) continuously decreased over the period. This result is due to total

household consumer expenditure was not spent on goods completely (also spent on

service), although dining out item rose with high rate of growth advances food scraps

generation. In addition, the result of prediction for relationship between total consumer

expenditure (TCE) and food scraps generation shows that in spite food scraps will

increase with TCE rising, but gradient of curve is flat.

The share of metal fraction of MSW rocket with consumer expenditure rising from

1999 to 2008 and figure 6-2 shows relatively less scatter, which means a general close

relationship between total household consumer expenditure and metal generation. The

result is inseparable from the increase in consumer durable goods containing a lot of

metal materials. Furthermore, percentage of metal fraction in MSW generation still will

rapidly upwards with TCE rising in the future 10 years (see Fig 6-2)..

Similarly, the share of glass fraction increased with consumer expenditure rising from

1999 to 2008, but figure 6-3 shows relatively more scatter, which means generally far

relationship between total household consumer expenditure and metal generation.

Types of the glass fraction mainly are the various forms of glass bottles in household

trash can, but much more bottles are recycling by scavenges, so statistic data of

environmental apartment did not include these glass bottles. Otherwise, from analysis

of chapter 5, because household more was spending on drinking containing a number of

glass materials, so share of glass fraction gradually increase over the period. In addition,

figure 6-3 also laid out that the share of glass fraction will increases steadily with TCE

rising in future 10 years(see Fig 6-3)..

As can be seen from figure 6-4, the share of paper has the fluctuant trend, but from the

overall trend it was exhibiting an increasing trend with TCE rising due to the use of raw

material as paper by most of the packaging industrials. But there is a fluctuation and

more scatter, it also affected by scavenges. In addition, share of paper fraction will

steady raise with TCE rising in future 10 years (see Fig 6-4).


28

In the latest 10 years, the share of plastic fraction increased rapidly due to consumption

growth, the main plastic fraction consisted of many plastic bags, a few plastic battles

and a few other types of plastic scraps. As can be seen from figure 6-5, it shows less

scatter, which means a relatively close relationship between total household consumer

expenditure and metal generation. Although most plastic battles generated by the

households are collected by scavenges, but many plastic fraction as plastic bags due to

households consumption rising enhances plastic bag production and consumption. So

that plastic battles were collected by scavenges make smaller impact on changes of the

share of plastic fraction. Moreover, the share of plastic fraction will swiftly rise in

future 10 years (see Fig 6-5).

Base above discussion, changes of waste composition will affect the waste

management. First of all, changes of waste compounds are more toward organic due

to food scraps, textile and paper of total waste generation continuously increase.

Waste management department would be take into account that and use more

organically disposal methods in the future. Second of all, more recycling waste are

generated, so waste management department also would be enhanced these materials

to recycle. Third of all, metal contains more hazard substances rapidly increase

require waste management department plan at aim that.

8. Conclusion

Trend of discretionary spending increased gradually changes consumption patterns of

household that household consumption was focusing dining out, dress and durable

goods; in addition, sampling analysis aimed at all Jinan or three different income level

district, they have similar regular, on the one hand, due to consumption rose, generated

more and more MSW; on the other hand, changes in consumption patterns improved

variations of waste composition: on the whole, high consumption enhanced share of

food scraps, metal, glass, paper and plastic, at the same time, reduces share of dust and

wood; moreover, household consumption brought more influences on the share of

metal and plastic fractions.


29

Acknowledgements

First of all, I would like to thank the division of Industrial Ecology in Royal Institute of

Technology (KTH) for giving me this opportunity to advance in the master program

Sustainable Technology. During this process, I Study the knowledge I acquired from

literature and field works help me understand in depth waste management system.

Secondly, I would like to thank Monika Olsson of Royal Institute of Technology for

their patient supervision. I also want to thank Jinan City Appearance & Environmental

Sanitation Administration Bureau, where I spent as an internship about half of two

months, as well as thanks Jinan Statistic Bureau for information about household

consumption.

Finally, I would like to thank my parents for their encouragements all the time.


30

Reference

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City Economic Statistic Yearbook 2000. (available in February 2009) (In Chinese)





Capital living expenditure composition of urban households, 2007. Jinan City

Economic Statistic Yearbook 2007. (available in February 2009) (In Chinese)

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Economic Statistic Yearbook 2008. (available in February 2009) (In Chinese)

Capital living expenditure composition of urban households in Changqing,

Huaiyin and downtown, 2008. Jinan City Statistic Yearbook 2008. (available in

February 2009) (In Chinese)

Waste composition of urban, 1999-2008. Jinan City Appearance & Environmental

Sanitation Administration Bureau. (available in March 2009) (In Chinese)

Waste composition in Changqing, Huaiyin and downtown, 2008. Jinan City

Appearance & Environmental Sanitation Administration Bureau. (available in

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Sifeng Liu, Julong Deng, The range suitable for GM(1,1), reserved 1999.

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Pilapiiya, Relation of waste generation and composition to socio-economic factors:

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online: 21 April 2007, Springer Science + Business Media B.V. 2007.

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composition, trend and impact of urban solid waste in Beijing, received 19 April

2006, accepted: 31 December 2006, published online: 15 May 2007 # Springer

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Jinan Government, www.jinan.gov.cn (available in May 2009) (In Chinese)

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(available in May 2009)

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http://www.unece.org/env/europe/consumption_patterns


32

Appendix 1 Capital living expenditure composition of urban households (Reference Jinan City Economic Statistic Yearbook 2000, 2004, 2006, 2007, 2008)

1999 2000 2001 2002 2003 2004 2005 2006 20007 2008

Per capita GDP(RMB) 15861 17001 18843 20807 23362 27610 31606 36394 42171 45724

Income(RMB) 7162 8471 8607 8982 11013 12005 13578 15340 18005 20802

Total 6415 6892 7110 7426 7995 8581 9227 10731 12390 13904

Food consumme

expenditure(%)34.4% 34.6% 34.1% 34.6% 32.9% 32.5% 33.0% 31.1% 31.5% 31.3%

Cereals(%) 3.1% 3.2% 3.0% 2.9% 2.7% 2.6% 2.5% 2.1% 2.1% 2.1%

Oil(%) 1.1% 1.1% 1.1% 1.1% 1.1% 1.1% 1.0% 0.7% 0.9% 0.8%

Meat and

Poultry(%)6.3% 6.0% 6.2% 6.0% 5.7% 5.4% 6.5% 4.5% 4.5% 4.4%

Eggs(%) 1.1% 1.2% 1.1% 1.2% 1.2% 1.2% 1.1% 0.9% 0.8% 0.8%

Aquatic

Products(%)2.1% 2.0% 1.9% 1.6% 1.5% 1.5% 1.6% 1.7% 1.7% 1.7%

Vegetable(%) 2.6% 2.5% 2.6% 2.6% 2.7% 2.7% 2.8% 2.9% 2.8% 2.9%

Saccharide(% 0.3% 0.4% 0.3% 0.3% 0.3% 0.3% 0.3% 0.2% 0.3% 0.2%

Drink(%) 1.5% 1.6% 1.7% 1.7% 1.9% 2.0% 2.1% 2.2% 2.4% 2.4%

Dry and Fresh

Fruits(%)2.1% 2.2% 2.3% 2.4% 2.5% 2.5% 2.9% 3.0% 2.9% 3.0%

Cake(%) 1.0% 1.0% 0.9% 0.9% 0.9% 0.9% 0.9% 0.8% 0.7% 0.7%

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

Dining out(%) 6.8% 6.7% 6.9% 6.9% 7.2% 7.3% 7.7% 8.3% 8.6% 8.2%

Capital living expenditure compisition of urban households

type


33

Type 1999 2000 2001 2002 2003 2004 2005 2006 20007 2008

Dr ess consummer

expendi t ur e( %)10. 8% 11. 0% 10. 5% 10. 1% 10. 0% 9. 9% 10. 0% 10. 4% 9. 9% 10. 3%

Househol d

Faci l i t i es, Ar t i cl es

consummer

11. 8% 11. 7% 9. 8% 9. 1% 7. 9% 6. 4% 5. 8% 5. 6% 7. 4% 7. 2%

Heal t hcar e consummer

expendi t ur e( %)6. 9% 6. 6% 7. 3% 7. 9% 8. 5% 8. 5% 8. 6% 8. 7% 7. 5% 8. 0%

Tr anspor t at i on and

communi cat i ons

consummer

expendi t ur e( %)

6. 7% 6. 7% 8. 8% 10. 9% 12. 8% 14. 6% 15. 7% 16. 6% 17. 0% 17. 0%

Edut ai nment and

cul t ur e ser vi ce

consummer

expendi t ur e( %)

15. 5% 15. 5% 15. 7% 15. 4% 14. 3% 14. 8% 12. 8% 13. 5% 12. 9% 12. 5%

Habi t at i on consummer

expendi t ur e( %)8. 5% 8. 5% 9. 0% 8. 8% 9. 7% 9. 9% 10. 7% 10. 6% 10. 7% 10. 6%

Mi scel l aneous

Commodi t i es

consummer

expendi t ur e( %)

5. 5% 5. 5% 5. 0% 4. 2% 3. 9% 3. 5% 3. 4% 3. 5% 3. 1% 3. 0%

Capital living expenditure compisition of urban households

Note: at current prices, % means percentage of total consumer expenditure.


34

Appendix 2 Capital living expenditure of urban households in Changqing, Huaiyin and

downtown in 2008 (Reference Jinan City Economic Statistic Yearbook 2008)

Number of sample

(households)

Income(RMB)

Total consumer

expenditure(RMB)Food consumme

expenditure(%)2548 39.4% 4393 30.6% 4981 27.1%

Cereals(%) 275 4.3% 358 2.5% 280 1.5%

Oil(%) 88 1.4% 121 0.8% 83 0.5%

Meat and Poultry(%) 430 6.6% 596 4.2% 515 2.8%

Eggs(%) 89 1.4% 116 0.8% 99 0.5%

Aquatic Products(%) 120 1.9% 270 1.9% 267 1.5%

Vegetable(%) 227 3.5% 348 2.4% 302 1.6%

Saccharide(%) 17 0.3% 32 0.2% 37 0.2%

Drink(%) 137 2.1% 208 1.5% 272 1.5%

Dry and Fresh Fruits(%) 214 3.3% 405 2.8% 506 2.8%

Cake(%) 59 0.9% 108 0.8% 127 0.7%

Milk(%) 160 2.5% 299 2.1% 300 1.6%

Dining out(%) 542 8.4% 1258 8.8% 1826 9.9%

6471 14362 18394

Capital living expenditure of urban households in Changqing, Huaiyin, downtown in 2008

Type Changqing district Huaiyin district Downtown

11031(low income) 21543(middle income) 31269(high income)

120 60 60


35

type

Household Facilities,

Articles and

Services consumme

402 6.2% 992 6.9% 1422 7.7%

Healthcare

consumme

expenditure

460 7.1% 1119 7.8% 1503 8.2%

Transportation and

communications

consumme

990 15.3% 2410 16.8% 3186 17.3%

Edutainment and

culture service

consumme

725 11.2% 1738 12.1% 2345 12.8%

Habitation

consumme

expenditure

633 9.8% 1560 10.9% 2038 11.1%

Miscellaneous

Commodities

consumme

144 2.2% 573 4.0% 576 3.1%

Capital living expenditure of urban households in Changqing, Huaiyin, downtown in 2008

Changqing district Huaiyin district Downtown

Note: % means percentage of total consumer expenditure


36

Appendix 3 Waste composition of urban from 1999 to 2008 (Reference Jinan City Appearance

& Environmental Sanitation Administration Bureau, 1999-2008)

Ceramic Plastic

Tile Rubber

1999 576583 39.0% 2.9% 41.9% 38.6% 7.3% 45.9% 0.1% 0.5% 3.6% 5.2% 1.3% 1.2% 12.0% 0.2%

2000 600990 40.5% 3.3% 43.9% 36.7% 7.1% 43.8% 0.1% 0.6% 3.7% 5.1% 1.4% 1.1% 12.0% 0.4%

2001 615936 41.3% 3.4% 44.8% 35.7% 6.5% 42.3% 0.1% 0.7% 3.9% 5.4% 1.4% 1.2% 12.6% 0.4%

2002 666535 42.5% 3.1% 45.6% 35.4% 6.1% 41.5% 0.2% 0.7% 3.9% 5.5% 1.3% 1.0% 12.5% 0.4%

2003 720551 42.8% 3.4% 46.2% 34.4% 5.7% 40.1% 0.2% 0.6% 3.8% 6.1% 1.4% 0.9% 13.1% 0.6%

2004 770533 44.8% 3.6% 48.3% 33.3% 5.1% 38.4% 0.2% 0.7% 3.7% 6.3% 1.5% 0.9% 13.3% 0.5%

2005 757995 45.2% 3.7% 48.9% 31.9% 4.9% 36.8% 0.2% 0.7% 3.5% 6.9% 1.6% 0.9% 13.8% 0.5%

2006 803016 46.4% 3.5% 49.9% 31.2% 4.7% 35.9% 0.2% 0.7% 3.8% 7.2% 1.4% 1.0% 14.1% 0.2%

2007 766500 47.4% 3.3% 50.8% 30.6% 2.2% 32.8% 0.3% 0.8% 4.7% 7.5% 2.5% 0.5% 16.3% 0.2%

2008 791640 48.8% 3.4% 52.3% 28.1% 2.4% 30.5% 0.4% 1.0% 5.1% 7.8% 2.5% 0.4% 17.1% 0.1%

Glass Paper TextileWood,

Bambo

Waste composition of urban from 1999 to 2008

Type

Annual

waste

output

Food scrap Street cleaning Recyclable

Vegetable Meat Total Dust Total OtherTotal Metal

Note: % means percentage of total MSW generation.


37

Appendix 4 Waste composition in Changqing, Huaiyin and downtown in 2008 (Reference

reference Jinan City Appearance & Environmental Sanitation Administration Bureau, 2008)

Ceramic Plastic

Tile Rubber

Changqing

distrct79497 44.7% 3.1% 47.8% 35.4% 6.1% 41.5% 0.2% 0.7% 3.9% 3.4% 1.3% 1.0% 10.4% 0.4%

Huaiyin

district114139 48.7% 3.9% 52.6% 20.7% 3.0% 23.6% 0.3% 1.1% 10.1% 8.5% 2.7% 0.6% 23.3% 0.4%

Downtown 167589 49.5% 5.8% 55.3% 10.7% 0.7% 11.4% 0.9% 3.1% 14.4% 10.6% 3.6% 0.3% 32.8% 0.6%

Glass Paper TextileWood,

Bambo

o and

Waste composition in Changqing, Huaiyin, downtown in 2008

Type

Annual

waste

output

（t/a)

Food scrap Street cleaning Recyclable

Vegetable Meat Total Dust Total OtherTotal Metal

Note: % means percentage of total MSW generation.


38

Appendix 5 description about GM(1,1)

Model summarizing

The grey model GM(1,1), i.e., a single variable first-order grey model, is summarized

as follows:

Step 1 for an initial time sequence

X (0)

={x (0)

(1), x (0)

(2), …, x (0)

(i), …, x (0)

(n)} (1)

where x (0)

(i) the time series data at time i, n must be equal to or larger than 4.

Step 2 On the basis of the initial sequence X (0), a new sequence X (1) is set up through

the accumulated generating operation in order to provide the middle message of

building a model and to weaken the variation tendency, i.e.

X (1)

={ x (1)

(1), x (1)

(2), …,x (1)

(i), …,x (1)

(n)} (2)

where

x (1)

(k) k=1,2, …,n (3)

Step 3 The first-order differential equation of grey model GM(1,1) is then the following

+aX (1)

=b (4)

and its difference equation is

x (0)

(k)+aZ(1)

(k)=b k=2,3,…,n (5)

and from Eq. (5), it is easy to get


39

= × (6)

where a and b are the coefficients to be identified.

Let

Yn=[x (0)

(2), x (0)

(3),… ,x (0)

(n)] T

(7)

B= (8)

Also take

(x (1)

(k)+ x (1)

(k+1)) k=1,2,…,(n-1) (9)

and

A= [a, b] T

(10)

where Yn and B are the constant vector and the accumulated matrix respectively. Z (1)

(K+1) is the (k +1)th

background value. Applying ordinary least-square method to Eq.

(6) on the basis of Esq. (7) (10), coefficient A becomes

A= (BTB)

-1B

TYn (11)

Step 4 Substituting A in Eq. (5) with Eq. (11), the approximate equation becomes the

following

~x

(0) (k+1)=(

~x

(1) (1)-b/a) × e

-ak + b/a (12)


40

where ~x

(1) (k+1)is the predicted value of

~x

(1) (k+1) at time (k+1). After the

completion of an inverse accumulated generating operation on Eq. (12), ~x

(0) (k+1), the

predicted value of ~x

(0) (k+1) at time (k+1) becomes available and therefore,

~x

(0) (k+1) =

~x

(1) (k+1) -

~x

(1) (k) (13)

Where k=0, 1, 2, 3…

Model validation

In general, there are two criteria used to validate the grey model GM(1,1).

The first one is the absolute error criterion, i.e.,

e(k)=x(0)

(k)- ~

x (0)

(k) k=1,2,…,n (14)

and the second one is the mean absolute error criterion, i.e.,

en= (15)

In the validation, these will be used to show the practical implementation of the theory.

Here posterior variance test theory will be presented.

residual test:

q (k )= x (0 )

(k)- ~

x (0 )

(k) (16)

relative residual test:

△k= (17)

mean of residual test:


41

ˉq= (18)

square of mean square deviation of residual test:

= ˉq] 2

(19)

mean of raw data:

ˉx= (20)

square of mean square deviation of series:

= ˉx] 2

(21)

ratio of residual test (ratio of square of mean):

C= (22)

infinitesimal error probability:

P=P { ﹤ 0.6745S1} (23)

When ratio of residual test C﹤ 0.35, infinitesimal error probability P﹥ =0.95, the model

accord with extra fine grade.

When a is specified value, if △﹤ a and △n﹤ a, so the model up to grade.


42

A a coefficient vector

B a accumulated matrix

e(k) the absolute error

en the mean absolute error

m a constant based on X(1)

n the sample number

X (0)

the initial time sequence

X(0

(i) the time series data at time i in X(0)

~x

(0) (k+1) the predicted value of x

(0) (k+1) at time (k+1)

X(1)

a new time sequence based on X(0)

x(1)

(i) the time series data at time i in X(1)

~x

(1) (k+1) the predicted value of x

(1) (k+1) at time (k+1)

Yn a constant vector

the (k+1)

th background value

q (k) the residual test

△k the relative residual test

ˉq the mean of residual test

the square of mean square deviation of residual test

ˉx the mean of raw data

the square of mean square deviation of series

grade relative error

△

ratio of residual test

C

infinitesimal error

probability P

Extra fine

grade

0.01 0.35 0.95

Fine grade 0.05 0.5 0.8

Level Ⅲ 0.1 0.65 0.7

Level Ⅳ 0.2 0.8 0.6

Table appendix 5- 1 Accuracy grade and critical value.


43

C the ratio of residual test

P the infinitesimal error probability

Table appendix 5- 2 Notations.


44

Appendix 6 prediction for total consumer expenditure

The definitions of all parameters show in table appendix 5-2 notations.

(1) Establish model

Yearx (0) (k) (total

consumer expenditure)x

(1) (k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1) (k+1))

1999 6415 6415

2000 6892 13307 -9861

2001 7110 20417 -16862

2002 7426 27843 -24130

2003 7995 35838 -31840.7

2004 8581 44419 -40128.65

2005 9227 53646 -49032.2

2006 10731 64377 -59011

2007 12390 76766 -70571.345

2008 13904 90670 -83718.27

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

T

b/a= -53491, we can get

x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a (24)

=59906e0.09723(t-1)

-53491 (25)

put t=1,2,3,…,10 into function(25)

x(1)

1=6415 x(1)

2=12532.3 x(1)

3=19274.12 x(1)

4=26704.44 x(1)

5=34893.5


45

Year x (1)

(k) ~x (1)

(k) Absolute error Relative error

1999 6415 6415 0 0.00%

2000 12532.23 13307 -774.77 6.18%

2001 19274.12 20417 -1142.88 5.93%

2002 26704.44 27843 -1138.56 4.26%

2003 34893.5 35838.4 -944.9 2.71%

2004 43918.78 44418.9 -500.12 1.14%

2005 53865.67 53645.5 220.17 0.41%

2006 64828.27 64376.5 451.77 0.70%

2007 76910.31 76766.19 144.12 0.19%

2008 90226.08 90670.35 -444.27 0.49%

Table appendix 6- 4 Once accumulation contrasts for TCE.

Year x (0)

(k) ~x (0)


1999 6415. 00 6415. 00 0 0.00%

2000 6117. 23 6892. 00 774.769 12.67%

2001 6741. 89 7110. 00 368.115 5.46%

2002 7430. 33 7426. 00 4.325 0.06%

2003 8189. 06 7995. 40 193.664 2.36%

2004 9025. 28 8580. 50 444.78 4.93%

2005 9946. 89 9226. 60 720.286 7.24%

2006 10962. 60 10731. 00 231.6 2.11%

2007 12082. 03 12389. 69 307.66 2.55%

2008 13315. 78 13904. 16 588.38 4.42%

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

For table appendix 6-4 and appendix 6-5, we can see that errors raw data contrasts to

analog data, their errors are very small so don’t need residual test correction.

(2) Validate model

x(1)

6=43918.78 x(1)

7=53865.67 x(1)

8=64828.27 x(1)

9=76910.31 x(1)

10=90226.08

Table appendix 6- 2 Once accumulation for TCE.

x(0)

1=6415 x(0)

2=6117.231 x(0)

3=6741.885 x(0)

4=7430.325 x(0)

5=8189.064

x(0)

6=9025.28 x(0)

7=9946.886 x(0)

8=10962.6 x(0)

9=12082.03 x(0)

10=13315.78

Table appendix 6- 3 Reducing value for TCE.


46

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 6415. 00 6415. 00 0. 00 02000 6117. 23 6892. 00 - 774. 77 0. 1266535462001 6741. 89 7110. 00 - 368. 12 0. 0546011982002 7430. 33 7426. 00 4. 32 0. 0005820742003 8189. 06 7995. 40 193. 66 0. 0236491012004 9025. 28 8580. 50 444. 78 0. 0492815742005 9946. 89 9226. 60 720. 29 0. 0724132162006 10962. 60 10731. 00 231. 60 0. 0211263752007 12082. 03 12389. 69 - 307. 66 0. 0254642642008 13315. 78 13904. 16 - 588. 38 0. 044186672

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

ˉx=ˉx= =9022.6 ˉ△= =0.042

ˉq (k) = = 4.4269

= ˉq] 2

=204363.8 = ˉx] 2=5608195.1

C= =0.19 P=P { ﹤ 0.6745S1} =1

We can see form above analysis that actual TCE contrast to analog TCE from 1999 to

2008, ˉ△<0.05 which accords with fine grade, C<0.35 and P=1, which accord with

extra fine grade.

(3)Prediction of TCE form 2009 to 2018

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


47

Appendix 7 prediction for total waste generation


(1)Establish model

Year Annual waste output

（t/a)

Average day output

（t/d)x

(1) (k) Z (1) (k+1)=1/2(x (1) (k)+ x (1)

(k+1))1999 576583 1602 1602

2000 600990 1669 3271 -2436.5

2001 615936 1688 4959 -4114.75

2002 666535 1826 6785 -5871.55

2003 720551 1974 8759 -7771.65

2004 770533 2111 10870 -9814.2

2005 757995 2077 12946 -11908.05

2006 803016 2200 15146 -14046.4

2007 766500 2125 17271 -16208.9

2008 791640 2199 19470 -18370.9

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a,b)

T


x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

= 44998 e0.0351(t -1)

-46600 (26)


x(1)

1=1602 x(1)

2=3323.933 x(1)

3=5107.38 x(1)

4=6954.537 x(1)

5=8867.68

x(1)

6=10849.17 x(1)

7=12901.44 x(1)

8=15027.03 x(1)

9=17228.55 x(1)

10=19508.71

Table appendix 7- 2 Once accumulation for waste generation.


48

Year x (1)

(k) ~x (1)


1999 1602 1602 0 0.00%

2000 3602 3323.933 278.067 7.72%

2001 5603 5107.38 495.62 8.85%

2002 7605 6954.537 650.463 8.55%

2003 9608 8867.68 740.32 7.71%

2004 11612 10849.17 762.83 6.57%

2005 13617 12901.44 715.56 5.25%

2006 15623 15027.03 595.97 3.81%

2007 17630 17228.55 401.45 2.28%

2008 19638 19508.71 129.29 0.66%

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

Year x (0)

(k) ~x (0)


1999 1602 1602. 00 0.00 0.00%

2000 1669 1721. 93 52.93 3.17%

2001 1687. 5 1783. 45 95.95 5.69%

2002 1826. 1 1847. 16 21.06 1.15%

2003 1974. 1 1913. 14 60.96 3.09%

2004 2111 1981. 15 129.85 6.15%

2005 2076. 7 2052. 27 24.43 1.18%

2006 2200 2125. 59 74.41 3.38%

2007 2125 2201. 52 76.52 3.60%

2008 2199 2280. 17 81.17 3.69%

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

For table appendix 7-4 and 7-5, we can see that errors raw data contrasts to analog data, their

errors are very small so don’t need residual test correction.

(2)Validate model

x(0)

1=1602 x(0)

2=1721.933 x(0)

3=1783.446 x(0)

4=1847.157 x(0)

5=1913.143

x(0)

6=1981.487 x(0)

7=2052.272 x(0)

8=2125.586 x(0)

9=2201.519 x(0)

10=2280.165

Table appendix 7- 3 Reducing value for waste generation.


49

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 1602.00 1602.00 0.00 0

2000 1669.00 1721.93 -52.93 0.031715398

2001 1687.50 1783.45 -95.95 0.056856889

2002 1826.10 1847.16 -21.06 0.011531132

2003 1974.10 1913.14 60.96 0.030878375

2004 2111.00 1981.15 129.85 0.06151208

2005 2076.70 2052.27 24.43 0.011762893

2006 2200.00 2125.59 74.41 0.033824545

2007 2125.00 2201.52 -76.52 0.036008941

2008 2199.00 2280.17 -81.17 0.036909959

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

ˉx= =1950.837 ˉ△= =0.0311

ˉq (k) = = -3.7969

= ˉq] 2

=5146.1153 = ˉx] 2=48191.5544

C= =0.326779 P=P { ﹤ 0.6745S1} =1

We can see form above analysis that actual waste generation contrast to analog waste

generation from 1999 to 2008, C<0.35, which accords with fine grade, ˉ△<0.035 and

P=1, which accord with extra fine grade.

(3)Prediction of waste generation form 2009 to 2018

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


50

Appendix 8 prediction for food scraps generation


(1) Establish model

Year x (0) (k) (food scraps) x (1)

(k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1) (k+1))

1999 671 671

2000 732 1403 -1036.85

2001 755 2158 -1780.36

2002 833 2991 -2574.66

2003 912 3903 -3447.19

2004 1020 4923 -4412.92

2005 1016 5939 -5430.69

2006 1097 7036 -6487.11

2007 1078 8114 -7574.90

2008 1149 9264 -8688.83

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

T


x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

=13164e0.05584(t -1)

-12493 (27)


x(1)

1=671 x(1)

2=1427.04 x(1)

3=2226.51 x(1)

4=3071.89 x(1)

5=3965.83

x(1)

6=4911.10 x(1)

7=5910.67 x(1)

8=6967.64 x(1)

9=8085.31 x(1)

10=9267.18

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


51

Year x (1)

(k) ~x (1)

(k) absolute error relative error

1999 671. 00 670. 92 0.08 0.01%

2000 1427. 04 1402. 78 24.26 1.70%

2001 2226. 51 2157. 94 68.57 3.08%

2002 3071. 89 2991. 37 80.52 2.62%

2003 3965. 83 3903. 01 62.81 1.58%

2004 4911. 10 4922. 83 -11.73 0.24%

2005 5910. 67 5938. 54 -27.88 0.47%

2006 6967. 64 7035. 68 -68.04 0.98%

2007 8085. 32 8114. 12 -28.81 0.36%

2008 9267. 18 9263. 54 3.64 0.04%

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

Year x (0)

(k) ~x (0)


1999 671 670. 92 0.08 0.01%

2000 756.04 731. 86 24.18 3.20%

2001 799.47 755. 16 44.31 5.54%

2002 845.38 833. 43 11.95 1.41%

2003 893.93 911. 64 17.71 1.98%

2004 945.27 1019. 82 74.55 7.89%

2005 999.56 1015. 71 16.15 1.62%

2006 1056.97 1097. 14 40.17 3.80%

2007 1117.68 1078. 44 39.24 3.51%

2008 1181.87 1149. 42 32.45 2.75%

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

generation.

For table appendix 8-4 and 8-5, we can see that errors raw data contrasts to analog data,

their errors are very small so don’t need residual test correction.

(2) Validate model

x(0)

1=671 x(0)

2=756.04 x(0)

3=799.47 x(0)

4=845.38 x(0)

5=893.93

x(0)

6=945.27 x(0)

7=999.56 x(0)

8=1056.97 x(0)

9=1117.68 x(0)

10=1181.87

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


52

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 671.00 670.92 0.08 0.000119225

2000 756.04 731.86 24.18 0.031982435

2001 799.47 755.16 44.31 0.055424219

2002 845.38 833.43 11.95 0.014135655

2003 893.93 911.64 -17.71 0.019811395

2004 945.27 1019.82 -74.55 0.078866356

2005 999.56 1015.71 -16.15 0.016157109

2006 1056.97 1097.14 -40.17 0.038004863

2007 1117.68 1078.44 39.24 0.035108439

2008 1181.87 1149.42 32.45 0.027456488

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

generation.

ˉx= =926.72 ˉ△= =0.03171

ˉq (k) = = 0.363

= ˉq] 2

=1302.81 = ˉx] 2=24259.04

C= =0.23 P=P { ﹤ 0.6745S1} =1

We can see form above analysis that actual food scraps generation contrast to analog

food scraps generation from 1999 to 2008, ˉ△<0.05, which accords with fine grade,

C<0.35 and P=1, which accord with extra fine grade.


year Food scraps generated (ton/day)

2009 1181.87

2010 1249.75

2011 1321.52

2012 1397.42

2013 1477.68

2014 1562.55

2015 1652.29

2016 1747.18

2017 1847.53

2018 1953.64

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


53

Appendix 9 prediction for metal generation


(1) Establish model

Year x (0) (k) (metal) x (1)

(k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1) (k+1))

1999 0.7 0.7

2000 1.0 1.8 -1.2503

2001 1.0 2.7 -2.2535

2002 1.3 4.0 -3.3694

2003 1.4 5.4 -4.6782

2004 1.6 7.0 -6.1778

2005 1.6 8.6 -7.8062

2006 1.8 10.4 -9.4964

2007 3.1 13.5 -11.9379

2008 4.0 17.5 -15.5131

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

T

b/a= -6.38117 we can get

x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

=8.14337e0.17831(t -1)

-6.38117 (28)


Table appendix 9- 2 Once accumulation for metal generation.

x(1)

1=1.76 x(1)

2=3.35 x(1)

3=5.25 x(1)

4=7.52 x(1)

5=10.24

x(1)

6=13.48 x(1)

7=17.36 x(1)

8=21.99 x(1)

9=27.53 x(1)

10=36.07


54

Year x (1)

(k) ~x (1)


1999 1.76 1.76 0.00 0.00%

2000 3.35 4.10 -0.75 22.29%

2001 5.25 6.29 -1.04 19.82%

2002 7.52 9.03 -1.51 20.07%

2003 10.24 11.99 -1.76 17.16%

2004 13.48 15.37 -1.89 14.03%

2005 17.36 18.69 -1.34 7.70%

2006 21.99 22.21 -0.22 1.02%

2007 27.53 28.38 -0.85 3.08%

2008 34.15 36.07 -1.93 5.64%

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

Year x (0)

(k) ~x (0)


1999 1. 76 1. 76 0.00 0.00%

2000 1. 59 2. 34 0.75 46.99%

2001 1. 90 2. 19 0.29 15.48%

2002 2. 27 2. 74 0.47 20.63%

2003 2. 71 2. 96 0.25 9.11%

2004 3. 24 3. 38 0.13 4.13%

2005 3. 88 3. 32 0.55 14.29%

2006 4. 63 3. 52 1.11 24.03%

2007 5. 54 6. 16 0.62 11.28%

2008 6. 62 7. 70 1.08 16.28%

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

For table appendix 9-4 and 9-5, we can see that errors raw data contrasts to analog data,

their errors are very small so don’t need residual test correction.

(2) Validate model

x(0)

1=1.76 x(0)

2=1.59 x(0)

3=1.90 x(0)

4=2.27 x(0)

5=2.71

x(0)

6=3.24 x(0)

7=3.88 x(0)

8=4.63 x(0)

9=5.54 x(0)

10=6.62

Table appendix 9- 3 Reducing value for metal generation.


55

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 1.762 1.7622

2000 1.59 2.3366 -0.747 0.469929542

2001 1.9 2.1938 -0.294 0.154753132

2002 2.271 2.7392 -0.4685 0.206324041

2003 2.714 2.9612 -0.2473 0.091123475

2004 3.244 3.3776 -0.134 0.041312122

2005 3.877 3.3227 0.5541 0.142927156

2006 4.634 3.52 1.1135 0.240315097

2007 5.538 6.1625 -0.6246 0.112786435

2008 6.619 7.6965 -1.0776 0.162806509

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

ˉx= =3.41 ˉ△= =0.19

ˉq (k) = = -0.19

= ˉq] 2

=0.39 = ˉx] 2=14.5

C= =0.165 P=P { ﹤ 0.6745S1} =0.8

We can see form above analysis that actual metal generation contrast to analog metal

generation from 1999 to 2008, ˉ△<0.2 which accords with Level Ⅳ grade, C<0.35

which accords with extra fine grade, and P=0.8, which accords with fine grade.


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


56

Appendix 10 prediction for glass generation


(1) Establish model

Year x (0) (k) (related consumer expenditure) x (1)

(k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1)

(k+1))1999 9 9

2000 10 18 -13.41

2001 11 29 -23.65

2002 12 42 -35.34

2003 13 54 -47.87

2004 14 68 -61.05

2005 15 83 -75.59

2006 15 98 -90.86

2007 17 115 -106.74

2008 21 136 -125.58

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

T

b/a= -99.98, we can get

x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

=108.63e0 .0 8 6 1 4 ( t -1 )

-99.98 (29)


x(1)

1=8.65 x(1)

2=18.42 x(1)

3=29.08 x(1)

4=40.69 x(1)

5=53.34

x(1)

6=67.13 x(1)

7=82.17 x(1)

8=98.55 x(1)

9=116.41 x(1)

10=135.88

Table appendix 10- 2 Once accumulation for glass generation.


57

Year x (1)

(k) ~x (1)


1999 8.65 8.65 0 0.00%

2000 18.42 18.16 0.2593 1.41%

2001 29.08 29.13 -0.05765 0.20%

2002 40.69 41.55 -0.86513 2.13%

2003 53.34 54.18 -0.84497 1.58%

2004 67.13 67.91 -0.77367 1.15%

2005 82.17 83.27 -1.10765 1.35%

2006 98.55 98.45 0.09835 0.10%

2007 116.41 115.03 1.38335 1.19%

2008 135.88 136.14 -0.26035 0.19%

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

Year x (0)

(k) ~x (0)


1999 8.65 8.65 0 0.00%

2000 9.77 9.51 0.2593 2.65%

2001 10.65 10.97 0.31695 2.98%

2002 11.61 12.42 0.80748 6.96%

2003 12.65 12.63 0.02016 0.16%

2004 13.79 13.72 0.0713 0.52%

2005 15.03 15.37 0.33398 2.22%

2006 16.39 15.18 1.206 7.36%

2007 17.86 16.58 1.285 7.19%

2008 19.47 21.11 1.6437 8.44%

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

For table appendix 10-4 and 10-5, we can see that errors raw data contrasts to analog

data, their errors are very small so don’t need residual test correction.

(2) Validate model

x(0)

1=8.65 x(0)

2=9.77 x(0)

3=10.65 x(0)

4=11.61 x(0)

5=12.65

x(0)

6=13.79 x(0)

7=15.03 x(0)

8=16.39 x(0)

9=17.86 x(0)

10=19.47

Table appendix 10- 3 Reducing value for glass generation.


58

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 8. 65 8. 65 0 02000 9. 77 9. 51 0. 2593 0. 0265333692001 10. 65 10. 97 - 0. 31695 0. 0297555342002 11. 61 12. 42 - 0. 80748 0. 0695503882003 12. 65 12. 63 0. 02016 0. 0015931222004 13. 79 13. 72 0. 0713 0. 0051693642005 15. 03 15. 37 - 0. 33398 0. 022215572006 16. 39 15. 18 1. 206 0. 0735994142007 17. 86 16. 58 1. 285 0. 0719484882008 19. 47 21. 11 - 1. 6437 0. 084436499

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

ˉx= =13.59 ˉ△= =-0.038

ˉq (k) = = -0.026

= ˉq] 2

=0.67 = ˉx] 2=11.51

C= =0.058 P=P { ﹤ 0.6745S1} =0.8

We can see form above analysis that actual glass generation contrast to analog glass

generation from 1999 to 2008, ̄ △<0.05 and P=1 which accords with fine grade, C<0.35,

which accord with extra fine grade.


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


59

Appendix 11 prediction for paper generation


(1) Establish model

Year x (0) (k) (paper) x (1)

(k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1) (k+1))

1999 58 58

2000 62 120 -89.04

2001 65 185 -152.82

2002 71 256 -220.91

2003 75 332 -294.03

2004 78 410 -370.59

2005 73 482 -445.98

2006 83 565 -523.68

2007 100 665 -615.08

2008 111 777 -720.97

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

T

b/a= -752,53, we can get

x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

=810,36e0.07044(t -1)

-752,53 (30)


x(1)

1=57.83 x(1)

2=116.97 x(1)

3=180.43 x(1)

4=248.32 x(1)

5=321.57

x(1)

6=399.96 x(1)

7=484.07 x(1)

8=574.32 x(1)

9=671.15 x(1)

10=775.05

Table appendix 11- 2 Once accumulation for paper.


60

Year x (1)

(k) ~x (1)


1999 57.83 57.83 0.0022 0.00%

2000 116.97 120.25 -3.2777 2.80%

2001 180.43 185.39 -4.9616 2.75%

2002 248.52 256.43 -7.9144 3.18%

2003 321.57 331.64 -10.0683 3.13%

2004 399.96 409.53 -9.5704 2.39%

2005 484.07 482.43 1.6381 0.34%

2006 574.32 564.93 9.385 1.63%

2007 671.15 665.23 5.9181 0.88%

2008 775.05 776.72 -1.6719 0.22%

Table appendix 11- 4 Once accumulation contrasts for paper.

Year x (0)

(k) ~x (0)


1999 57.83 57.83 0 0.00%

2000 59.14 62.42 3.2805 5.55%

2001 63.46 65.14 1.6814 2.65%

2002 68.09 71.04 2.94809 4.33%

2003 73.06 75.21 2.15701 2.95%

2004 78.39 77.90 0.4919 0.63%

2005 84.11 72.89 11.21643 13.34%

2006 90.25 82.50 7.7468 8.58%

2007 96.83 100.30 3.4669 3.58%

2008 103.90 111.49 7.5893 7.30%

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

For table 6-41 and 6-42, we can see that errors raw data contrasts to analog data, their errors are

very small so don’t need residual test correction.

(2) Validate model

x(0)

1=57.83 x(0)

2=59.14 x(0)

3=63.46 x(0)

4=68.09 x(0)

5=73.06

x(0)

6=78.39 x(0)

7=84.11 x(0)

8=90.25 x(0)

9=96.83 x(0)

10=103.90

Table appendix 11- 3 Reducing value for paper.


61

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 57. 83 57. 83 0 02000 59. 14 62. 42 - 3. 2805 0. 0554699772001 63. 46 65. 14 - 1. 6814 0. 0264970592002 68. 09 71. 04 - 2. 94809 0. 043298742003 73. 06 75. 21 - 2. 15701 0. 0295253522004 78. 39 77. 90 0. 4919 0. 0062752112005 84. 11 72. 89 11. 21643 0. 1333565182006 90. 25 82. 50 7. 7468 0. 0858401632007 96. 83 100. 30 - 3. 4669 0. 035802842008 103. 90 111. 49 - 7. 5893 0. 073044273

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

ˉx= =77.5 ˉ△= =0,049

ˉq (k) = = -0.16681

= ˉq] 2

=28.25327 = ˉx] 2=6241.749

C= =0.067 P=P { ﹤ 0.6745S1} =0.8

We can see form above analysis that actual paper generation contrast to analog paper

generation from 1999 to 2008, ˉ△<0.05 and P=0.8 which accords with fine grade,

C<0.35, which accord with extra fine grade.


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


62

Appendix 12 prediction for plastic generation


(1) Establish model

Year x (0) (k) (paper) x (1)

(k) Z (1) (k+1)=-1/2(x (1) (k)+ x (1) (k+1))

1999 84 84

2000 85 169 -126.35

2001 91 260 -214.47

2002 100 359 -309.63

2003 121 480 -419.89

2004 134 614 -547.32

2005 143 757 -685.78

2006 158 915 -836.08

2007 160 1075 -994.95

2008 171 1246 -1160.72

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

BT=

BTB=

(BTB)

-1=

Yt=

So, BTYt=

(BTB)

-1 B

TYt= = (a, b)

Tb/a= -897,3, we can get

x(1)

t=[x(0)

1-(b/a)]*e-a(t-1)

+b/a

=980,92e0.08696(t -1)

-897,3 (31)


x(1)

1=83.62 x(1)

2=172.74 x(1)

3=269.96 x(1)

4=376.01 x(1)

5=491.69

x(1)

6=617.89 x(1)

7=755.55 x(1)

8=905.71 x(1)

9=1069.52 x(1)

10=1248.21

Table appendix 12- 2 Once accumulation for plastic generation.


63

Year x (1)

(k) ~x (1)


1999 83.62 83.62 0.0044 0.01%

2000 172.74 169.08 3.6642 2.12%

2001 269.96 259.86 10.1009 3.74%

2002 376.01 359.39 16.6199 4.42%

2003 491.69 480.40 11.2938 2.30%

2004 617.89 614.24 3.648 0.59%

2005 755.55 757.32 -1.7727 0.23%

2006 905.71 914.84 -9.1266 1.01%

2007 1069.52 1075.07 -5.5475 0.52%

2008 1248.21 1246.37 1.8442 0.15%

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

Year x (0)

(k) ~x (0)


1999 83.62 83.62 0 0.00%

2000 89.12 85.45 3.667 4.11%

2001 97.22 90.79 6.4291 6.61%

2002 106.05 99.52 6.52665 6.15%

2003 115.68 121.01 5.3284 4.61%

2004 126.19 133.84 7.6432 6.06%

2005 137.66 143.08 5.4253 3.94%

2006 150.17 157.52 7.3539 4.90%

2007 163.81 160.23 3.5841 2.19%

2008 178.69 171.30 7.3896 4.14%

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

generation.

For table appendix 12-4 and 12-5, we can see that errors raw data contrasts to analog

data, their errors are very small so don’t need residual test correction.

(2) Validate model

x(0)

1=83.69 x(0)

2=89.12 x(0)

3=97.22 x(0)

4=106.05 x(0)

5=115.68

x(0)

6=126.19 x(0)

7=137.66 x(0)

8=150.17 x(0)

9=163.81 x(0)

10=178.69

Table appendix 12- 3 Reducing value for plastic generation.


64

Year x (0)

(k) ~x (0)

(k) q (k)= x (0)

(k)- ~

x (0)

(k) △ k=∣ (x (0) (k)- ~x (0) (k))/x (0) (k)∣

1999 83.62 83.62 0 0

2000 89.12 85.45 3.667 0.041146861

2001 97.22 90.79 6.4291 0.06613171

2002 106.05 99.52 6.52665 0.061543662

2003 115.68 121.01 -5.3284 0.046059996

2004 126.19 133.84 -7.6432 0.060566967

2005 137.66 143.08 -5.4253 0.039411068

2006 150.17 157.52 -7.3539 0.048971772

2007 163.81 160.23 3.5841 0.021879737

2008 178.69 171.30 7.3896 0.041353907

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

ˉx= =124,82 ˉ△= =0,043

ˉq (k) = = 0,18457

= ˉq] 2

=33,48 = ˉx] 2=940,1

C= =0.19 P=P { ﹤ 0.6745S1} =0.7

We can see form above analysis that actual plastic generation contrast to analog plastic

generation from 1999 to 2008, ˉ△<0.05, which accords with fine grade, C<0.35, which

accord with extra fine grade and P=0,7, which accords with Level Ⅲgrade.


Year Plastic genetated (ton/day)

2009 195

2010 213

2011 232

2012 253

2013 276

2014 301

2015 328

2016 358

2017 391

2018 426

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

TRITA-IM 2009:11 ISSN 1402-7615 Industrial Ecology, Royal Institute of Technology www.ima.kth.se

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