Household Waste Production and
Categorization in Grahamstown, Eastern
Cape
Final 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 Department
3rd October 2014
Word Count: 6,779
ABSTRACT
4
1) INTRODUCTION
5
1.1) Waste Generation of Households
5
1.2) Waste Composition of Households
6
2) KEY QUESTIONS, HYPOTHESES AND OBJECTIVES
6
2.1) Key Questions and Hypotheses
6
2.2) Objectives
7
3) STUDY AREA
8
4) METHODOLOGY
10
4.1) Data Collection and Analyses
10
4.2) Data Collection and Analyses
11
4.3) Assumptions and Pitfalls
12
5) RESULTS
13
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
14
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
6.3.1 Concluding statement
6.3.2 Improvements and Suggestions For Future Research
25
25
26
6.4) Recommendations
26
2
REFERENCE LIST:
28
PLAGIARISM STATEMENT
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Abstract
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). Socioeconomic 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 as household
income and waste production, hence waste generation was considered a function of affluence.
5
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 lowincome 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).
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
6
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.
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).
7
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 Somerset
heights to represent the high-income households (Figure 2).
8
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
eventually determine whether an individual’s perceptions, attitudes and practices ultimately
11
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).
4.3) Assumptions and pitfalls
12
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.
5) Results
13
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
8
Person Per Household
7
6
5
4
3
2
1
0
High-Income
Medium-Income
Low-Income
Study Site
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 highincome 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 mediumincome 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.
5.2) Average total waste for a household in each study site
14
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.
12
Waste (kg)
10
8
6
4
2
0
High-Income
Medium-Income
Low-Income
Study Site
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).
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5.3) Waste composition
The results in Figure 5 show that the composition going from largest to smallest for the highincome 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.
Waste Composition (%)
60
50
40
High-Income
30
Medium-Income
20
Low-Income
10
0
Wet
Paper
Glass
Plastic
Metal
Catogories of waste
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 highincome, 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.
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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
Support
Income Class
A
Sort/Separate Garbage (%) 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.
3
Waste (kg)
2.5
2
1.5
1
0.5
0
1-2
3-4
5- 6
7-8
9-10
Household Size (Persons Per Household)
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.
18
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.
3
Waste (kg)
2.5
2
1.5
1
0.5
0
Very Low
Low
Average
High
Environmental Awareness Level
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).
4.5
4
Waste (kg)
3.5
3
2.5
2
1.5
1
0.5
0
Tertiary
Secondary
Primary
Education level
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 highincome 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 follow the expected pattern
22
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 larger household
23
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
24
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 transdisciplinary 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 influenced
25
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 government
26
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 :
- 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.
27
- 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
Reference List:
Attari, S. Z. 2014. Perceptions of water use. Proceedings of the National Academy of
28
Sciences, 111(14): 5129-5134.
Barr, S. 2007. Factors Influencing Environmental Attitudes and Behaviors: A UK Case Study
of Household Waste Management", Environment and Behavior, 39(4): 435-473.
Bolaane, B., and Ali, M. 2004. Sampling household waste at source: lessons learnt in
Gaborone. Waste management & research, 22(3): 142-148.
Browne, S. 2001. Household waste generation, composition and recycling in Grahamstown,
a small town in South Africa. Grahamstown: Rhodes University
(HonoursDissertion)[pdf].
Cossu, R., and Masi, S. 2013. Re-thinking incentives and penalties: Economic aspects of waste
management in Italy. Waste management, 33 (11): 2541-2547
Dawnarian, R. B. 2004. An investigation of solid waste management practices: the case of
the Chatsworth Township in metropolitan Durban - KwaZulu-Natal. (MSc
thesis)[pdf].
Dennison, G. J., Dodd, V. A., and Whelan, B. 1996. A socio-economic based survey of
household waste characteristics in the city of Dublin, Ireland—II. Waste quantities.
Resources, Conservation and Recycling, 17(3): 245-257.
Diatz L. F, Savage G. M., Eggerth L. L, and Golouke C. G. 1993. Composting and recycling:
Municipal solid waste. Lewis Publishers, London.
Dyson, B., and Chang, N. B. 2005. Forecasting municipal solid waste generation in a fastgrowing urban region with system dynamics modeling. Waste management, 25(7):
669-679.
29
Emery, A. D., Griffths, A. J., and Williams, K. P. 2003. An in depth study of the effects of
socio-economic conditions on household waste recycling practices. Waste
management & research, 21(3): 180-190.
Etengeneng, D. 2012. Municipal solid waste management in Grahamstown, republic of South
Africa.Novia. 1-41.
Fox, R. 2012. Grahamstown’s Urban Development: Maps, Photo Pairs, Excursion Guide.
[Online]. Available:http://roddyfox.wordpress.com/2012/10/11/grahamstowneurban-development-maps-photo-pairs-excursion-guide/ [Accessed 25/03/14].
Google Earth V6.2.2.6613 (24 October 2013). Grahamstown, South Africa. 33°18’22.40’’S,
26°32’08.63’’E, Eye Altitude 4.31 km. NES/Astruim 2014. Available:
http://www.earth.google.com. [Accessed 30/09/2014].
Hoornweg, D., and Bhada-Tata, P. 2012. What a waste: a global review of solid waste
management. World Bank: Washington, DC.
Irvine, P.M. 2012. Post-apartheid Racial Integration in Grahamstown: A Time-geographical
Perspective. Grahamstown: Rhodes University (MSc thesis)[pdf].
Karani, P., and Jewasikiewitz, S. M. 2007. Waste management and sustainable development
in South Africa. Environment, Development and Sustainability, 9(2): 163-185.
Lober, Douglas J. 1996. Municipal solid waste policy and public participation in household
source reduction. Waste Management & Research. 14 (2): 125-143.
Makana Municipality. 2013. IDP Municipal Statistics: Types of Dwellings. [Online].
Available: http://www.makana.gov.za/wp-content/uploads/2013/07/Types-pfdwellings.pdf [Accessed 25/03/14].
30
Palmer Development Group. 1996. Evaluation of solid waste practice in developing urban
areas of South Africa. Annual Report of the Water Research Commission. Pretoria:
Palmer Development Group Printer.
Parfitt, J., Flowerdew, R., and Doktor, P. 1994. Socio-economic variables in household waste
modeling: Two case studies. The centre for social and economic research on global
environment, 94(02): 1-50.
Patton, M. Q. 2005. Qualitative research. In Everitt, B., and Howell, D. (eds), Encyclopaedia
of Statistics n Behavioural Science. London: John Wiley and Sons, Ltd. P. 123-129.
Sivakumar, K., and Sugirtharan, M. 2010. Impact of family income and size on per capita
solid waste generation: A case study in Manmunai North Divisional Secretariat
Division of Batticaloa. Journal of Science of the University of Kelaniya Sri Lanka, 5:
13-23.
Slack, R. J., Gronow, J. R., and Voulvoulis, N. 2005. Household hazardous waste in municipal
landfills: contaminants in leachate. Science of the total environment, 337 (1): 119-137.
Statistic South Africa. 2013. Local Municipality; Makana Municipality. [Online]. Available:
http://beta2.statssa.gov.za/?page_id=993&id=makana-municipality [Accessed
02/04/14].
Qu, X. Y., Li, Z. S., Xie, X. Y., Sui, Y. M., Yang, L., and Chen, Y. 2009. Survey of
composition and generation rate of household wastes in Beijing, China.Waste
Management, 29 (10): 2618-2624.
Van Beukering, P., Sehker, M., Gerlagh, R. and Kumar, V. 1999. Analysing urban solid
waste in developing countries: a perspective on Bangladore, India. Working paper
NO. 24. Institute for social and economic change (ISEC), Nagarbhavi, India.
World Bank Group. 2012. World Development Indicators. [Online]. Available:
http://data.worldbank.org/sites/default/files/wdi-2012-ebook.pdf . [29/09/2014].
31