Household Waste Production and Categorization in Grahamstown, Eastern

CapeFinal Report

Group 1

Authors:

Bridget Nkoana (G12N1746)

Francina Teffo (G11T2942)

Luke Maingard (G12M0104)

Slie Sithole (G11S1371)

Stuart Biesheuvel (G11B2242)

Tadiwanashe Dune (G12D2893)

Tanya Kuhlmann (G12K0431)

Environmental Science Department3 rd October 2014

Word Count: 6,779

ABSTRACT 4

1) INTRODUCTION 4

1.1) Waste Generation of Households 5

1.2) Waste Composition of Households 5

2) KEY QUESTIONS, HYPOTHESES AND OBJECTIVES 6

2.1) Key Questions and Hypotheses 6

2.2) Objectives 7

3) STUDY AREA 7

4) METHODOLOGY 10

4.1) Data Collection and Analyses 10

4.2) Data Collection and Analyses 11

4.3) Assumptions and Pitfalls 13

5) RESULTS 14

5.1) Persons Per Household in Each of the Three Study Sites 14

5.2) Average Total Waste for a Household in Each Study Site 15

5.3) Waste Composition 16

5.4) Levels of Recycling and Sorting 17

5.5) Average Total Waste Per Household Size 18

5.6) Environmental Awareness 19

5.7) Education Level 20

6) DISCUSSION 21

6.1) The Influence of Social-Economic Factors on the Amount of Household Waste Being Produced 21

6.2) The Influence of Peoples Perceptions, Attitudes, and Practices on Waste Production 23

6.3 Conclusion and Recommendations 256.3.1 Concluding statement 256.3.2 Improvements and Suggestions For Future Research 26

6.4) Recommendations 26

2

REFERENCE LIST: 29

PLAGIARISM STATEMENT 32

TURN-IT-IN REPORT 33

Abstract

3

Municipal household solid waste production and categorisation has become a predominant

issue of public concern in both South Africa and at a global scale (Browne, 2001). Socio-

economic and demographic factors such as globalisation, population growth, unemployment

and rapid urbanisation have influenced the production of municipal solid waste (Browne,

2001; Bolaane and Ali, 2004). The study was conducted in Grahamstown, Eastern Cape.

Income, education, household size and environmental awareness were the variables used to

determine their influence on waste production and categorisation. A questionnaire survey and

a ‘sort’ and ‘weigh’ technique was used to acquire the data. Three socio-economic areas were

sampled resulting in a total of 96 houses. The five categories focused on were for sorting and

weighing were paper, glass, plastic, metal, and organic waste. Two statistical tests were used

to analyse the data; an ANOVA one-way analysis of variance test and multiple linear

regressions.

The results show that high-income areas produce the least waste (2.64 kg) compared to

medium-income (6.81 kg) and low-income (6.6 kg). The results between household size and

levels of waste produced showed there was no statistical difference. Also, there was no

statistical difference between the amount of waste a person produces, and their level of

environmental awareness. The results showed that a statistical difference was only found

between a person with primary education (2.24 kg) and a person with secondary education

(1.47 kg). Our hypothesis was that socio-economic factors, perceptions and practices do

influence waste production. However, many of the results deviated from the anticipated

expectations. It was found that an increased number of factors are needed in future research

to establish a sound, definitive conclusion of what exactly affects waste production.

4

1) Introduction

Municipal solid waste production has become a predominant issue of public concern in South

Africa and at a global scale (Browne, 2001). Socio-economic and demographic factors such

as globalisation, population growth, unemployment, and rapid urbanisation contribute to the

excessive production of municipal solid waste (Browne, 2001; Bolaane and Ali, 2004). In

general, the higher the state of economic development and the rate of urbanisation the greater

the quantity of solid waste produced (World Bank, 2012). Moreover, with increasing

revenues the consumption of goods and services, living standards, and the amount of waste

produced increases (Van Beukering et al., 1999).

Waste production at a household scale is an important aspect to waste management and

categorisation. This project has focused on some of the key aspects that may influence waste

production and categorisation such as: location (socio-economic class), income, household

size, education levels, practices, attitudes, and perceptions.

1.1) Waste generation of households

The current waste production levels at a global scale amount to 1.3 billion tonnes per year

with 3 billion residents producing approximately 1.2 kg per person per day (World Bank,

2012). However with rapid urbanization urban populations are anticipated to increase further

by 1.3 billion to 4.3 billion residents by 2025. Consequently waste generation trends will also

increase from 1.2 to 1.42 kg per person per day by 2025 whilst annual waste generation levels

increase to 2.2 billion tonnes respectively (World Bank, 2012). With these increases, more

waste dumps will need to be created, resulting in more land being used and more pollution

will be produced and released.

In Africa, household waste generation is estimated to range between 0.4 to 1.1 kg per day,

spanning into 2.4 kg per day in urban areas and much lower in poorer residential areas

(World Bank, 2012). This can be attributed to various socio-economic statuses, such as

income status of different households as seen in a study that was done by the World Bank

(2012) and the Palmer Development Group (1996). In the Palmer Development Group study

(1996) it was found that there is a direct relationship between socio-economic groupings such

5

as household income and waste production, hence waste generation was considered a

function of affluence.

1.2) Waste composition of households

Household waste composition in developing countries is mainly composed of 57% organic

material, paper 9%, plastic 13%, glass and metals 8%, and other elements of waste 13%

(World Bank, 2012). According to the Palmer Development Group (1996), waste production

from high-income and medium-income households reflect that of developed countries whilst

that from low-income households reflects that of developing countries. In this regard, high-

income earning households tend to produce more packaging waste such as paper and plastics.

This is due to the fact that high-income and medium-income households tend to buy more

pre-cooked foods which consequently have a relatively high disposable packaging content

than the low-income earning households and informal areas who often prepare every basic

meal at home (Bolaane and Ali, 2004).

The challenge regarding waste in South Africa is waste classification. One of South Africa’s

challenges is identifying and categorizing household waste (Karani and Jewasikiewitz, 2007).

There are laws and regulations implemented to address this challenge, however many of the

laws are not implemented because of the lack of knowledge of them. The South African

National Waste Management Strategy has been developed to address the gaps in general

waste classification and household waste generation (Karani, and Jewasikiewitz, 2007).

2) Key Questions, Hypotheses and Objectives

2.1) Key questions and hypotheses

The first key question was how social-economic factors influenced the composition and

amount of household waste being produced. We hypothesized that the type and amount of

waste produced is a function of socio-economic status. We hypothesized this according to

Slack et al. (2005). There is a strong correlation between income level and waste production:

as people earn more, their consumption of electricity and foodstuffs increase resulting in

increased waste (Slack et al., 2005).

6

Another relation to socio-economic status of households was that households that have a

medium to high-income rate tend to manage their waste products more efficiently compared

to that of lower-income households. Lower-income households cannot afford large amounts

of these goods and therefore wasted produce will be less (Cossu, 2013).

The second key question asked was whether people’s perceptions, attitudes and practices

influenced the waste produced. We hypothesized that people’s perceptions, attitudes, and

practices will influence waste production and the type of waste produced. The reason for this

hypothesis was based on the level of education and environmental awareness that an

individual had (Etengeneng, 2012). According to Etengeneng (2012), people that tend to have

had higher levels of education tend to have a more positive attitude and practice towards

waste management because of their increased knowledge on waste issues.

Thus people with higher levels of education and environmental awareness have proven to

have a more positive influence in waste production, sorting, and recycling (Parfitt et al.,

1994). Conversely people with a lower standard of education tend to have an ignorant or

oblivious view towards waste production and its impact on the ecosystem around them

(Parfitt et al., 1994).

2.2) Objectives

There are three objectives to be determined in the course of the year project. The first

objective was to determine which socio-economic factors influence waste production and

categorisation? This is a necessary objective, as it will assist in accepting or rejecting the first

hypothesis.

The second objective was to determine whether people’s attitudes, perceptions, and practices

affect waste production and categorisation? This provided an insight concerning the second

hypothesis (Parfitt et al., 1994). According to Parfitt et al. (1994) peoples’ attitude towards

waste issues have a large influence on the amount of waste they produce as well as how the

household manages waste produce.

7

The last objective was to determine whether waste categorisation is a function of knowledge.

This will determine environmental awareness of an individual and the ecosystem around

them (Etengeneng, 2012).

3) Study Area

The study was conducted in Grahamstown, Eastern Cape, which is under the Makana

Municipality. Established in 1812, Grahamstown now has a population of approximately

50 000 people of which 73% are black, 12% are white, 14% are colored and 1% are

Indian/Asian (Makana Municipality, 2013). These demographics are shown in Figure 1 (Fox,

2012; Google Earth V6.2.2.6613, 2013). According to Statistic South Africa (2013) the

population is made up of 6.2% elderly people, 69.4% working age (15-64) people and 24.4%

of population is young (0-14).

Eastern Cape is one of the poorest provinces in South Africa, with a very low employment

rate of 32. 5% (Statistics South Africa, 2013). Grahamstown has 29% employed people, 42%

unemployed and 29% people which are not economically active (Statistics South Africa,

2013). According to Irvine (2012), 93.7% of citizens of the town who are 20 years and older

have completed primary school, 35.6% have completed secondary education, 22.9% have

completed Matric and 12% have some form of higher education. Irvine (2012) states that

only 6.3% people who are 20 years and older have no form of schooling. Makana

Municipality has 85.4% formal dwellings with an average household size of 3.4 people and

44.5% of households are female headed (Statistics South Africa, 2013). Socio-economic

status is a factor that contributes to where people are spatially located in area. (Irvine, 2012).

The geography and historical events of Grahamstown have been highly influential in the race

distribution (Irvine, 2012). Policies applied during the apartheid era such as the Group Areas

Act 41 of 1950 enforced segregation of different racial ethnicity (Figure 1) and as a result this

has shaped the past and present landscape of South Africa (Irvine, 2012). Irvine (2012) stated

that although a lot has changed spatially and politically, radical distinct economic and social

differences within geographical boundaries still reflect the colonial and apartheid legacy as

seen in Figure 1 (Irvine, 2012). Residential areas and schools remain tied to the apartheid’s

divisions of race (Irvine, 2012). For the purpose of the study three spatial sites related to

former segregation boundaries were selected (Figure 2). The sampling sites included Fingo

Village for low-income households, Hillsview for medium-income households, and lastly

8

Somerset heights to represent the high-income households (Figure 2).

Figure 1: Map representing the different socio-economic classes of Grahamstown, Eastern Cape

during the apartheid (Fox, 2012; Google Earth V6.2.2.6613, 2013).

Figure 2: Map of Grahamstown, Eastern Cape (Google Earth V6.2.2.6613, 2013)

9

4) Methodology

4.1) Data collection and analyses

Our first key question asks how social economic factors influence the amount of household

waste being produced. The main variables that were measured were; income, gender

composition, age, education, household size, social class, and lastly the type of waste that

was produced.

There were two primary methods for measuring the variables; a questionnaire survey and a

‘sort’ and ‘weigh’ technique. The questionnaire survey was used in order to cover all five of

the variables measured. The variables that were covered by the questionnaire were; income,

gender composition, age, education, household size and environmental knowledge. The ‘sort’

and ‘weigh’ technique was used to cover the quantity of waste variables.

In a previous study done by the International Solid Waste Association (I.S.W.A), for

sampling household waste in Gaborone, Botswana, 47 households were sampled utilizing

seven categories over a period of 21 days (Bolaane and Ali, 2004). In our study, we reduced

the number of categories of waste to five, limiting the ‘sort’ and ‘weigh’ technique method to

one week, and increased the total number of households sampled to 96. This change to our

study has enabled our ‘sort’ and ‘weigh’ technique to provide improved and more accurate

results, as through having more raw data to sort, thus having more information to test,

therefore allowing for more accurate results.

The five categories our study used for sorting and weighing was paper, glass, plastic, metal,

and organic waste. These categories of waste were sorted and weighed individually. The

participants were asked whether they were willing to take part in the questionnaire survey and

also separating their waste into ‘wet’ and ‘dry’ waste for a period of one week in order to aid

our research project for the ‘sort’ and ‘weigh’ technique. If the participants were unwilling to

take part in the questionnaire survey, upon permission we took their current rubbish bags at

their household; in order for the group members to sort out the waste, followed by weighing

the different categories of waste, to determine the level of household waste produced.

10

Based on the limitations of the research project such as, unfavorable weather conditions as

well as the absence of household members at the property at the time of arrival of the group

members. Thus the questionnaire survey had to be conducted over a period of one week, at

different times throughout the week depending on the amount/availability of time the

independent group member had to conduct the survey to the households, and collect the

rubbish bags upon the second return to the specific household, during the data collection

phase. The questionnaire was provided to the participants during the first visit to their

premises.

After the sort and weigh technique on the categories of waste within the rubbish bags was

completed and the data recorded, the next phase was data analysis. We used two techniques;

an ANOVA one-way analysis of variance test and multiple linear regression. We used these

tests to compare the data collected from the ‘sort and weight technique’ as well as the

information from the surveys of the households from different classes, to compare and

analyze variances of two or more samples in order determine if the samples came from

different populations.

The study took into account proportional sampling of households in each of the socio-

economic classes of households to increase accuracy of results. The sample size was

calculated by examining the Makana Municipality (2013) ‘Annual Report on Household

Income, and Report on Types of Dwellings’. We found that there are an estimated 13,433

homes in Grahamstown. There are 3,040 homes in the low-income class, 8,305 homes in the

low to medium-income class, and 2,080 homes in the medium to high-income class. Our aim

was to test at least 1% of the population, resulting in a sample of 30 low-income households,

37 medium-income households, and 29 high-income households. In total, 96 households were

sampled using the questionnaire survey and the ‘sort’ and ‘weigh’ technique.

4.2) Data collection and analyses

The second key question asked whether people’s perceptions, attitudes, and practices

influenced waste production.

The variables that were measured for the second key question were education, environmental

awareness, attitudes towards waste, and quantity of waste. These variables enabled us to

11

eventually determine whether an individual’s perceptions, attitudes and practices ultimately

influenced their waste production.

The method used for collecting the data in this key question was distributing the

questionnaire survey to the participants, in order to gain information and insight on the level

of environmental awareness of the household. The questionnaire survey covered all the

variables mentioned apart from quantity of waste, which was established by the ‘sort’ and

‘weigh’ technique method that was discussed in the previous key question above.

In order to determine whether the resident of the household was environmentally aware, we

used environmentally based questions, asked in a section of the questionnaire survey.

Examples of these questions were whether the participant was aware of global warming,

recycling, or whether they perceived waste as a current issue. Based on their answers for this

section of the questionnaire survey, we gave them a score out of four, this score determined

the households’ environmental awareness. This technique is very similar to that which Attari

(2014) used in his ‘Perceptions of Water Use’ publication. Attari (2014) asked participants to

estimate the number of gallons of water used by 17 different activities, which was then used

to determine how aware a participant was of water use.

The data analysis phase consisted of content analysis. We chose to do this because the

variables in the second key question were qualitative and not quantitative, as in the first key

question (Bolaane and Ali, 2004). The content analysis was used in order to tease out the key

themes, patterns, understandings and insights (Patton, 2005).

The sample size was randomly selected using the method Browne (2001), used. Erf numbers

were taken from the houses in the three individual social classes. Unlike house numbers, Erf

numbers do not repeat. We placed the Erf numbers into an Excel program. As a group, using

the Microsoft Excel program, we randomly selected the relevant number of houses for each

social class we had to sample. If we arrived at a household and the potential participant was

not home, we had a pre-determined resolution to move on and attempt to survey the house to

the left of the unsuccessfully surveyed house hold (Browne, 2001).

12

4.3) Assumptions and pitfalls

We had three assumptions. Firstly we assumed that respondents would be willing to

participate in our study. Our second assumption was that the participants will answer

truthfully and not be influenced to simply give an answer that is socially acceptable. Lastly

we assumed that a respondent would be home upon our arrival. If the respondent was not in

their home, the group would move onto another house in the same area to interview another

potential respondent. These assumptions are vital in ensuring the success of our research.

We have determined three possible pitfalls. The first pitfall was the language barrier. This

was a pitfall in our group as no members speak isiXhosa as a home language fluently. This

pitfall was overcome by hiring a translator. The second pitfall was participants could feel

pressure to give expected or ‘correct’ answers. This was overcome by ensuring them that the

questionnaire is fully anonymous. The last pitfall was if there is a repelling attitude and

unwillingness to participate in our study, as this inhibited us from gaining crucial data

imperative to our study as well as costing precious survey time. Our study relied on

participation from the general public. We were able to prevent this pitfall by ensuring a strict

code of ethics during the study as well as being appreciative of any input or time a participant

is able to give.

If any of our assumptions were not true or if our pitfalls did occur and our respondent was

uncomfortable, we simply proceeded to the next household.

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5) Results

5.1) Persons per household in each of the three study sites

The result in Figure 3 illustrates that the average number of persons per household was 2.1

(SD±0.939) in the high-income study site, 3.7 (SD±1.270) in the medium-income study site,

and 4.5 (SD±2.255) in the low-income study site

High-Income Medium-Income Low-Income0

0.51

1.52

2.53

3.54

4.55

Study Site

Pers

on P

er H

ouse

hold

Figure 3: Graph illustrating the average number of persons per household in the three study

sites.

The average persons per household was statistically significant (P<0.01) between the high-

income and medium-income sites. Results between the high-income and low-income sites

were also statistically significant (P<0.01), whereby the high-income site was lower. The

medium-income and low-income sites yielded statistically insignificant results at the 5%

level. This shows that the high-income study site had significantly fewer persons per

household than the medium and low-income study sites.

14

5.2) Average total waste for a household in each study site

The results in Figure 4 indicate that houses located in the high-income site had an average

total waste of 2.64 kg (SD±1.262). Houses located in the medium-income site produced 6.81

kg (SD±2.763) of waste on average, and houses in the low-income site produced an average

of 6.60 kg (SD±3.794) waste.

High-Income Medium-Income Low-Income0

1

2

3

4

5

6

7

8

Study Site

Was

te (k

g)

Figure 4: Graph illustrating the average total waste for a household in the high-income site,

medium-income site, and low-income site.

The ANOVA test found that houses in the high-income site produced significantly less waste

than the medium-income or low-income site (P<0.01). There was no statistically significant

difference between the amount of waste produced in the medium-income and low-income

sites (P>0.05).

15

5.3) Waste composition

The results in Figure 5 show that the composition going from largest to smallest for the high-

income site is wet waste, glass, paper, plastic, and then metal. The medium-income site is

wet, glass, metal, paper, and then plastic. Lastly the low-income site is wet, paper, glass,

plastic, and then metal.

Wet Paper Glass Plastic Metal0

10

20

30

40

50

60

High-IncomeMedium-IncomeLow-Income

Catogories of waste

Was

te C

ompo

sition

(%)

Figure 5: Graph illustrating the average composition of waste for the three study sites.

The largest proportion was wet waste. The proportion of wet waste was 41.65% in the high-

income, 39.47% in medium-income, and 59.05% in low-income. The proportion of Glass was

also large in the high-income (23.36%) and medium-income (26.67%). The smallest

proportion of waste was metal, which was 6,85% in high-income, and only 2.72% in low-

income. This indicates that there were differences in not only the proportions of each waste,

but also different proportions between the three study sites. This result is vital in the study as

It focuses on both the production, and composition of waste.

16

5.4) Levels of recycling and sorting

The level or recycling and sorting results in Table 1 show that out of the households who

recycle in the high-income site, 70.83% always recycled and the remaining 29.16% recycle

often. In terms of the medium-income site, 61.54% always recycle, 23.08% recycle often, and

25.38% recycle sometimes. In the low-income site, out of the households who recycle, 50%

always recycle, and 50% only recycle sometimes.

Table 1: Table illustrating the percentage households who sort/separate their garbage and

percentage households who support a recycling program

Income ClassSort/Separate Garbage

(%)

Support A

Recycling Program

(%)

High-Income 82,76 58,62

Medium-Income 35,15 21,62

Low-Income 13,33 6,57

It is observed that as you move from the high-income site down to the low-income site, the

level of garbage being separated and sorted decreases. This decreasing trend is also shown in

supporting a recycling program. Nearly all of the support for a recycling program was for

CSD (a recycling initiative at Rhodes University).

17

5.5) Average total waste per household size

The effect of household size on waste indicated is indicated in Figure 6. On average

households with 1-2 persons produced 1.78 kg (SD±0.959) of waste, households with 3-4

persons produced 1.7 kg (SD±0.744), households with 5-6 persons produced 1.36 kg

(SD±0.402), households with 7-8 persons produced 1.54 kg (SD±0.365), and households with

9-10 persons produced 1.3 kg (SD±0.116) of waste.

1-2 3-4 5- 6 7-8 9-100

0.20.40.60.8

11.21.41.61.8

2

Household Size (Persons Per Household)

Was

te (k

g)

Figure 6: Graph illustrating the average total waste per household size.

The ANOVA test produced no statistically significant results between the data (P>0.05). This

means that although there is a slight variation in waste levels shown in Figure 6, there is in-

fact no statistical difference between household sizes and levels of waste produced.

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5.6) Environmental awareness

The average level of waste produced from persons with varying environmental awareness

level is illustrated below in Figure 7. The results found that on average persons with a very

low environmental awareness produced 1.15 kg (SD±0.486) of waste, low environmental

awareness produced 1.64 kg (SD±0.936) of waste, average environmental awareness

produced 1.73 kg (SD±0.768) of waste, and high environmental awareness produced 1.49 kg

(SD±0.580) of waste.

Very Low Low Average High0

0.20.40.60.8

11.21.41.61.8

2

Environmental Awareness Level

Was

te (k

g)

Figure 7: Graph illustrating the average level of waste produced from persons with various

levels of environmental awareness.

The ANOVA test produced statistically insignificant results (P>0.05). This indicated that

there is no statistical difference between the amount of waste a person produces, and their

level of environmental awareness.

19

5.7) Education level

The varying levels of waste produced from persons with different education levels ranging

from tertiary, secondary and primary are illustrated in Figure 8. The average waste produced

for a person with a tertiary education was 1.67 kg (SD±0.679), secondary education is 1.47

kg (SD±0.725), and primary education was 2.24 kg (SD±1.612).

Tertiary Secondary Primary0

0.5

1

1.5

2

2.5

Education level

Was

te (k

g)

Figure 8: Graph illustrating the average level of waste produced from persons with different

education levels.

An ANOVA test found that the only statistically significant difference was between persons

with a secondary and primary education (P<0.05). The result between persons with tertiary

compared to other education levels was statistically insignificant (P>0.05). This result

indicates that a person with a primary education was likely producing more waste than a

person with a secondary education, but no more than a person with a tertiary education.

20

6) Discussion

6.1) The influence of social-economic factors on the amount of household waste being

produced

One of the factors that the project looked at was household waste production in different

income areas classified as low, medium and high-income areas. According to Sivakumar and

Sugirtharan (2010), income levels affect waste production. As illustrated medium income

area produced more waste of about 6.81kg. One of the reasons for this is that residents in

middle income areas can do without leftovers whether in the form of food, packaging, worn

out clothes or energy (Dyson and Chang, 2005). Another reason is that middle-income

households are more wasteful and as a result they end up producing unnecessary waste. This

means that since middle-income households produce more waste unnecessarily because they

know that they can afford to (Dyson and Chang, 2005).

There was a statistical significance difference between the high income and low-income area.

Household waste production in high-income area was low as resulted by Figure 3, by a

proportion of 2.64 kg. One of the factors that account for less waste being produced by high-

income households is that waste management in high-income areas is more efficient and

adequately facilitated. This includes sewage systems, sanitation disposals that are rarely

provided in low income areas. Sanitation systems found in high-income areas reduce waste

production, where else low income areas lack sanitation disposals that add more to waste

(Dyson and Chang, 2005). As illustrated by Figure 1, there are statistically less people living

in the high-income area than the medium-income and low-income area. This explains why

there is less waste produced in the high-income area and more waste in the low-income area

per household. This is very important when designing policies that have to do with the

redistribution of income.

Generally speaking, household income is directly proportional to waste generation per capita

(Diaz et al., 1993); however the correlation between wet waste, glass, paper and plastic waste

generation is different from that between household waste generation and income (Qu et al.,

2009). This means that family income has been found to be negatively related to household

wet waste generation and positively related to household waste generation of paper, plastics

21

and glass (Qu et al., 2009; Hoornweg and Bhada-Tata, 2012). Furthermore, when a

households’ level of affluence increases so does the level of inorganic waste (newspapers,

plastics, boxes, glass etc.) volumes produced (Hoornweg and Bhada-Tata, 2012). This can be

seen in Figure 5 of our results which accord with the studies done by Hoornweg and Bhada-

Tata (2012) and Qu et al. (2009).

The above mentioned trends, can be attributed to the fact that; high-income households tend

to purchase more prepared foods and ready-made food stuffs which result in less food-related

discards such as peels, pits etc. (Hoornweg and Bhada-Tata, 2012). Moreover, households

with more income tend to have more opportunities to dine in restaurants (Qu et al., 2009).

Households with low-income waste on the other hand constitute the largest proportion of wet

waste, which was expected for low- household waste composition in developing countries

(Boolane and Ali, 2012). The high wet waste proportions in low-income households can be

attributed to different living and dietary habits, these include their preference on preparing

every basic meal at home, mostly characterised by their staple diet; porridge.

According to Etengeneng (2012) people with a higher education status tend to have a more

positive attitude and practice towards waste management because of their higher knowledge

on waste issues, thus people with a higher education status and environmental awareness

have proven to have a more positive influence in waste production, sorting, and recycling

(Parfitt et al., 1994). While on the other end of the scale people with a lower level of

education tend to have an ignorant or oblivious nature towards waste production and its

impact on the ecosystem around them and may not be aware of the positive environmental

attributes of proper waste disposal and recycling (Parfitt et al., 1994; Etengeneng, 2012).

Education levels and socio-economic factors are highly connected to and influence each

other. According to Dennison et al. (1996) education level and its interaction with socio-

economic factors are likely to have a negative relationship with the amount of waste

produced. In the study conducted a person with primary education produced more waste than

a person with tertiary education, while a person with secondary education produced the least

as seen in Figure 8. Figure 8 also shows that the statistical significance in waste produced,

was only between waste produced by a person with secondary education and a person with

primary education. The waste produced by a person with tertiary education compared to

other education levels was statistically insignificant. Therefore, waste production does not

22

follow the expected pattern mentioned by Dennison et al. (1996). Instead the results obtained

from the study mostly highlighted what Parfitt et al. (1994) and Etengeneng (2012) stated

about education and waste management, because from the study people with tertiary

education knew more about solid management bylaws and how they work, they had more

environmental knowledge, and they also sorted and recycled their waste more than other

education levels and this decreased with level of education.

This was attributed to the fact that, higher education levels result in an increase in positive

perceptions, recycling behavior, and environmental awareness (Etengeneng, 2012). The

problem was that most of these factors mentioned did not significantly affect the amount of

waste produced by people with different levels of education; this maybe be contributed to

location. Location might influences people’s behavior and perception, for an example, in the

study people with tertiary education in high-income area were more likely to follow the

norms of their location than a person with tertiary education in medium and low-income areas

that were likely to follow habits of their area. Evidently from the study 75% of people with

tertiary education in low income area did not sort their waste and the rest who did, they did

not do it often and all of the above participants did not know how environmental soli

management bylaws work. To reach a conclusive statement about location more studies need

to be conducted. The difference in habits towards household waste management might be

highly influenced by the difference in management plan and time and financial investment

the Makana municipality has for each location. The other possible influence was economical

because people with tertiary education are likely to have good jobs, therefore earning more

allowing them to buy more products contributing to their total waste production. They are

many factors that add and remove from the waste produced by a person or household, that

looking at one factor at a time is not enough, so it would been good if considered all of the

influencing factors at once.

6.2) The influence of peoples perceptions, attitudes, and practices on waste production

Referring back to Figure 6, which showed the average waste per person in various household

sizes, it was shown that there was more waste being produced by households with fewer

people than those living in larger households. This was not the expected result, as the

relationship between the amount of people in the household and waste produced predictably

would be a positive relationship. Thus, implying that waste per person would be less in a

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larger household as waste was spread across more people. According to Emery et al. (2003)

however, waste is never consistent and changes throughout the year due to a variety of factors

such as food availability, change of season and even temperature. This will effect what

people buy and thus the type and amount of waste generated in the house hold.

This result also contradicts the paper by Lober (1996), which explained that waste per person

may demonstrate a significant difference between households with varying amounts of

people. This is because larger households usually buy in more bulk, thus meaning less actual

packaging waste per person (Lober, 1996). It is for this reason that the statistically

insignificant results in Figure 6 are unexpected.

Other factors that this study focused on were the influences that people’s practices and

perceptions may have on waste production. A study carried out by Dyson and Chang (2005)

found that practices such as recycling had an influence on waste production. The trends found

were a positive relationship between income and the practice of recycling of newspaper

(Dyson and Chang, 2005). It was also stated that households in higher income areas practice

recycling more than in medium or low income areas (Dyson and Chang, 2005). Table 2

which illustrate figures on the percentages of people that practice recycling and waste sorting

showed that the highest amount of recycling and waste sorting occurred in the high income

area of our study area. This directly reflects on the results found in the study carried out by

Dyson and Chang (2005). We could assume that people in the high income areas recycle

more and sort and separate their garbage because we also found that in the high income areas,

up to 90% of the people had tertiary education levels, meaning in the learning time they must

have learnt of the importance and need to recycling and waste sorting or separation.

Barr (2007) explains that the environmental values and understandings that people have

influences the way in which they manage and produce waste. This study found that the

behaviors and attitudes of people influence how they dispose their waste. This involves more

than the way they perceive environmental problems but other factors like policy knowledge,

environmental knowledge and waste knowledge, all of which were examined in our study.

Given the results in Figure 6, we find that the level of environmental awareness that people

have does not influence the amount of waste that they generate. No statistical difference was

shown between the different levels of environmental awareness that people had because

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regardless of their environmental awareness score, they produced relatively the same amount

of waste.

Conversely, Barr (2007) explains that those whose environmental awareness is higher, will be

more proactive in terms of how their waste is both produced and managed because in most

cases they are more open to change and are more “one with nature”. In addition,

environmental awareness was said to give light on how an individual will treat the

environment. In our study however, this was not the case because regardless of the level of

environmental awareness, the amount of waste produced had no statistical significance. We

found that the basis upon which we measured environmental awareness could have been too

broad to stand as a factor that may influence waste production. This is because we made use

of our own made analysis for environmental awareness, which only asked yes/no questions

on whether people knew about basic environmental issues. This test of environmental

awareness did not take past experiences, religion, motives for being environmentally aware or

any psychological variables into account. Environmental awareness and the attitudes and

perceptions of people all have much more intricate detail such as psychological variables

(Barr, 2007). This involves factor likes previous experiences, personal feelings towards the

issue, personal behavior, and intrinsic motivation and personality characteristics such as

positive feelings for recycling or expectance of praise/reward or recognition (Barr, 2007).

6.3 Conclusion and recommendations

6.3.1 Concluding statement

The results found in this study deviated from those that were assumed or anticipated. We

found that the way in which people manage their waste goes beyond where they live, how

much they earn or how many people live in the home. It also goes further than their

connection with the environment and their environmental knowledge. Although these would

be the most obvious indicators, they fall short of including past experiences and the

psychological factors of the people who produce the waste in the home. This shows the need

for a much more trans-disciplinary approach to the study.

We reject both our hypotheses. In key question one; although income and household size

influenced waste production, education levels did not. In key question two, practices

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influenced waste production but perceptions and environmental awareness did not. Carrying

out this intensive study has led to the conclusion that several factors influence household

waste. Having only focused on three socio-economic factors in conjunction with a general

focus on practices and perceptions, the study may have fallen short of having had a well-

rounded and detailed enough investigation to conclude on what truly influences of waste

production and categorisation in different homes.

6.3.2 Improvements and suggestions for future research

There were a variety of factors with which when taken into consideration, could have

improved the overall results of this project. Survey design is an important aspect of

determining the amount of waste that is generated at source. For this project, the socio-

demographic characteristics such as monthly income, age, gender and education status,

should not have been only limited to the heads of households, who were the only people

interviewed in this project. Other members of the household such as the spouse of head,

children, and grandchildren should have been interviewed or rather, their socio-demographic

characteristics recorded in the recording sheet. Other factors that this study could have

improved on include the sampling size. The sampling size could have been increased from

120 to 140 households so as cover for those households who were: absent during the survey

collection day, households whose gates were locked during the collection day and households

whose wastes were collected by the local municipality before we had access to them. These

factors reduced our sample size of 120 to 96 households.

Suggested variables for further research are; the age of the people in the household and how it

affects the amount of waste produced. The gender of the people in the household and how it

affects the amount of waste produced and the last variable of how seasonality affects the

amount of waste generated in a household will further benefit the study and generate more

accurate results.

6.4) Recommendations

According to (Dawnarain, 2004) solid waste generation and composition are two major

important factors in designing the cost effective and environmental compatible solid waste

management system. The best way to reduce the cost of waste management for the

26

government and to reduce environmental impact of waste is to reduce waste produced at the

source and improve waste composition. There are many general ways of reducing waste such

as recycling, creating a compost pile, re-using items you usually throw away, bringing less

waste at home, buying in bulk and using reusable containers. But off course as general as

these suggestions are people usually do not know about them and Makana Municipality and

Rhodes University as the main education institution in Grahamstown should be responsible

for informing people.

They should be involved in the promotion of environmental education, information

and capacity building in communities.

Most people did not know about the bylaws, there is a need for Solid waste

management policy programme to educate the public on their right to environmental

issues.

They should host Meetings and environmental workshops to teach people about

appropriate environmental skills.

They should support community-based initiatives that seek solutions to waste

management, sanitation and access to resources; some areas feel neglected by

municipality.

They should involve stakeholders in early stages of the planning process in order to

encourage public input and acceptance of the solid waste management plans and to

increase good attitudes and participation. To not only getting information out but to

also retrieve information about issues concerning each area. For an example most

people wanted dumping points in low income area but others said they create more

pollution and they become a dumping point for dead animals.

High income areas have an environmental committee (SCD) but there is a Lack of

awareness of environmental committee in the areas so it’s less effective. The middle

income and low income area do not have environmental committees so one should be

established for them.

Most people said they do not sort because of lack of refuse bags, so the municipality

should Increase extra refuse bags of household and garden waste

They should establish programmes that focus on reduction and separation of waste

material at source for example :

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- Composting programmes: Composting divert organic waste from households,

refuse of high organic matter content reduce waste that goes to the landfill sites thus

reducing waste disposal cost.

- Recycling programmes: to also reduce waste that goes to the landfill site, give

Incentives for people who recycle and they should use the incentive to buy bags and

keep the rest. People suggested that they should be an Informal collection of waste for

recycling needs because they do not have time or transport to take the waste to

recycling centres .

- Education on good and bad waste management: teach people to favour product

with high recycled content, Choose rechargeable batteries and long-life bulbs

maintain your possessions by repairing and keeping them in good condition

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