Unequal Burdens: How Socio-Demographic Variables Shape the Environmental, Health, and Socio-Economic Effects of Illegal Waste Dumping

Abstract

Illegal waste dumping (IWD) remains a major challenge for many developing municipalities, contributing to environmental degradation, public health risks, and socio-economic burdens. This study aims to assess the environmental, health, and socio-economic impacts of IWD and to examine the influence of socio-demographic variables (gender, education, age, and income) on perceived impacts. Primary data was collected through a quantitative survey of 381 participants from the Thabazimbi Local Municipality. The Mann–Whitney test was used to compare perceived IWD impacts between gender groups, and the Kruskal–Wallis test was used to compare perceived IWD impacts across education, age, and income groups. The results showed strong agreement (>80%) on the perceived environmental health and socio-economic impacts of IWD, indicating that IWD is a universal challenge across the municipality. Moreover, the statistical analysis revealed that income and education groups differed in their perceived environmental and socio-economic impacts (p < 0.05), respectively, although the differences were minimal across the impact statements. The study provides valuable insights by integrating environmental, health, and socio-economic effects of IWD across various socio-demographic groups. In doing so, municipalities can develop more sustainable waste management systems that reduce IWD and support broader sustainability objectives, including environmental protection, public health improvement, and socio-economic development.

1. Introduction

Illegal waste dumping (IWD) is a widespread phenomenon affecting both developed and developing countries, persisting despite the existence of municipal waste management systems. IWD is driven by factors such as population growth [1], limited or absent municipal waste services [2], and community perceptions [3]. Studies have shown that IWD exacerbates environmental degradation and poses significant threats to public health [4,5]. For example, Bangani et al. [6] found that IWD affects the Mthatha River in South Africa, resulting in poor water quality, which is more pronounced during wet seasons as rainwater carries pollutants from dumping sites into the watercourse. Notably, this study argued that although the impacts may not be immediately apparent, they have lasting effects on stream biodiversity and surrounding communities. In fact, Mbanjwa [7] found that IWD practices by fishermen in the Eastern Cape Province of South Africa adversely affect riverine biodiversity, as heavily polluted sites have shown decreased species richness and lower organism abundance. These dumping sites have long-term impacts on all aspects of the non-living environment. Chen et al. [8] found that in China, areas with IWD exhibit decreased soil bacterial diversity with increasing levels of pollution, resulting in widespread soil contamination and negatively affecting vegetation. Similarly, in Slovakia, IWD disrupts the species composition of native vegetation and creates conditions for the proliferation of invasive species [9]. IWD has also been found to negatively affect air quality in areas where it is prevalent. For instance, residents of Cape Town, South Africa, reported encountering unpleasant odors that negatively impacted air quality, therefore posing a risk to their health [2]. The aforementioned environmental deterioration and declining ecosystems are likely to have significant health effects on surrounding communities.

These health effects include respiratory issues, diarrhoea, skin rashes, and cuts from sharp objects at dumping sites. A study by Khumalo et al. [10] in Bulawayo, Zimbabwe, found that residents living within a 90-m buffer zone around dumpsites had the highest incidence of diarrheal disease compared with other residents. This suggests that communities living near IWDs are more vulnerable to adverse health effects, as these sites are characterised by the leaching of metal concentrates into the groundwater. A study by Breg et al. [11] in Ljubljana, Slovenia, found that a relatively low concentration of contaminants from dumping sites rendered drinking water unsuitable for consumption in the area. This suggests that even a small concentration of contaminants from IWDs can have profound health implications for surrounding communities. Moreover, the accumulation of contaminated water from clogged drainage systems creates a breeding ground for mosquitoes and rats, which can carry infectious diseases [2]. This is consistent with the findings of Duh et al. [12], who found that areas with large dumping sites and significant human influence have the highest prevalence of rodent infection. However, these dumping sites are not only breeding grounds for rats and mosquitoes but also attract children, serving as playgrounds and thus providing a direct pathway for exposure to the physical and biological hazards of waste. Such exposure increases the risk of diseases, skin infections, and injuries, which often require medical treatment. The additional, unexpected financial cost creates strain, exacerbating existing inequalities for low-income residents, who must choose between seeking medical aid and purchasing food [1,13]. These findings are consistent with those of Senekane [14] and Yap et al. [15], who found that the additional, unexpected financial burden of healthcare exacerbates poverty and worsens socio-economic vulnerability. Overall, the findings indicate that IWD not only causes health burdens but also has socio-economic impacts on affected communities.

The above studies indicate that certain community groups experience the impacts of IWD more than others, constituting environmental injustice [16]. As noted by Ramachandran et al. [17], environmental injustice refers to the unequal distribution of environmental risks and benefits across social groups that face environmental harm and have limited access to environmental benefits due to structural inequalities and exclusion from environmental decision-making. Understanding environmental justice and opposing unequal or discriminatory exposure to environmental hazards is crucial for protecting the environment and human health [18]. Several studies (e.g., [1,9,19]) have assessed the environmental, health, and socio-economic impacts of IWD in peri-urban communities and developed countries. However, there is limited empirical evidence on how socio-demographic variables such as gender, education, age, and income influence perceptions of these impacts in a South African context. Therefore, this study aims to assess the environmental, health, and socio-economic impacts of IWD in the Thabazimbi Local Municipality, with a specific focus on how socio-demographic variables influence residents’ perceptions of these impacts. This study will contribute to empirical evidence on IWD by providing insights from a developing municipality characterized by service delivery challenges and spatial inequalities that shape waste management outcomes. Rather than examining the associated impacts in isolation, as in other perception studies, this study adopts an integrated approach to assess environmental, health, and socio-economic impacts simultaneously. Moreover, the study empirically examines how socio-demographic variables influence residents’ perceptions. Therefore, the study contributes to the environmental justice discourse by highlighting perception inequalities and offers policy-relevant insights for designing targeted and inclusive municipal waste management interventions. The main hypothesis (H1) guiding this study is that:

H1. 

There is a significant relationship between socio-demographic variables (gender, age, education, and gender) and perceived environmental, health, and socio-economic impacts of IWD.

2. Literature Review

Various studies, such as Ngalo and Thondhlana [1], Cutter et al. [20], and Haywood et al. [21], have shown that socio-demographic characteristics, including gender, education, age, and income, play a crucial role in shaping exposure to and perceptions of IWD. These variables are discussed below.

2.1. Gender Dimensions of IWD

Gender has been examined as a variable in various social studies because it is believed to shape experiences and responses to environmental phenomena. According to WHO [22], gender refers to the socially constructed characteristics of women and men, including norms, roles, and relationships. Women are believed to have a stronger sense of environmental responsibility and different perceptions from men, based on their primary household responsibilities [23]. Furthermore, Adeoye et al. [24] found that gender plays a crucial role in waste management, providing a clear understanding of men’s and women’s perceptions to achieve sustainable waste management practices. This aligns with Sustainable Development Goal (SDG) 5, which aims to achieve gender equality by addressing systemic inequalities that limit the participation of women, girls, and communities in decision-making [25]. This inclusion will not only assist in achieving gender equality but also help to achieve sustainable waste management, as all perspectives are considered. Ref. [26] reported that women in San Lorenzo South, Sta. Rosa City were more aware and knowledgeable about proper waste management than their male counterparts. As a result, they were more likely to have positive perceptions of waste management. In a similar study Ref. [27] compared male and female trainees’ attitudes toward environmental protection, based on personal, psychological, and socio-cultural variables, and found that female respondents had a more favorable attitude than male respondents.

These findings collectively highlight that gender not only influences behavioural practices around waste but also the way people perceive the impact of IWD. While studies have reported gender differences in environmental risk perception and waste-related attitudes, some studies indicate that gender does not significantly influence perceived impacts or related behaviours. For example, Ref. [28] found that gender does not significantly influence solid waste disposal practices among Opol Community College students in the Philippines, thereby challenging the studies above regarding gender-specific behaviours in waste management. Similarly, Ref. [29] found that gender does not significantly influence levels of environmental concern in Sylhet City, Bangladesh. The above literature underscores the importance of considering individual differences and factors beyond gender in research. Based on inconsistent findings regarding the influence of gender on environmental risk perception, some studies report notable variations across gender groups, while others report no significant differences. Accordingly, this study posits the following hypothesis:

H2. 

There is no significant difference between males and females in their perceived impact of IWD in the Thabazimbi Local Municipality.

2.2. Education and IWD

Education is one of the most commonly used socio-demographic variables in social science research, as it captures variation in access to information and socio-economic status, both of which can influence awareness and perceptions of social and environmental issues [30]. Regarding IWD, it is believed that residents’ educational attainment influences their disposal behaviour, perceptions, and knowledge. A study by Musasa [31] in Chegutu, Zimbabwe, found that educational level does, in fact, influence knowledge of solid waste management, as residents with higher education were more likely to understand the adverse impacts of IWD. This is supported by studies indicating that a lack of education and knowledge influences residents’ disposal behaviour and perceptions. In rural Limpopo, South Africa, a lack of awareness and education was found to be a key driver of poor perceptions and continued dumping behaviour [14]. This pattern is supported by the literature review by [32], who found that education is directly proportional to environmental attitudes, knowledge, and practice regarding environmental sustainability issues; the lack of such influences disposal behaviour. However, knowledge alone does not necessarily translate to action; residents may be aware of the impacts of IWD but continue to engage in the practice due to institutional gaps. This was witnessed in Joe Slovo Park, South Africa, where residents are aware of the environmental and health impacts of IWD; however, they continue the practice because of a lack of waste infrastructure, such as bins and refuse bags for collection [2]. These findings are supported by Tuu [33], who found that despite adequate knowledge of the implications of IWD, residents of the Ahafo Region, Ghana, continue to participate in the practice.

These findings imply a gap between residents’ knowledge and their actual disposal behaviour. This is supported by evidence that education does not influence perceptions and disposal behaviour. The findings of studies by Anokye et al. [34] in the Kassena Nankana East Municipality in Ghana and [35] in the Vhembe District, South Africa, revealed a weak correlation between educational attainment and effective waste management practices, as higher educational attainment did not consistently predict sustainable waste disposal practices in these areas. This implies that residents tend to dispose of waste illegally, regardless of their educational status, especially when no other options are available. Based on inconsistent findings regarding the influence of education on environmental risk perception, some studies report notable variations across education groups, while others report no significant differences. Accordingly, this study posits the following hypothesis:

H3. 

There is no significant difference among education groups in their perceived impact of IWD in the Thabazimbi municipality.

2.3. Age and IWD

Age is among the most commonly used dependent variables in research, especially in social and environmental research, to explain differences in attitudes, behaviours, and decision-making patterns [36]. In the context of IWD, it helps identify which age groups are more likely to engage in or oppose IWD, understand perceived impacts, and determine influencing factors [37]. It is believed that older individuals are more likely to experience the detrimental health impacts of IWD than younger individuals, due to prolonged exposure to polluted environments [38]. For instance, ref. [39] reported that individuals aged 36–50 in Bloemfontein, South Africa, had a high prevalence of respiratory symptoms, including wheezing, due to residing close to dumping sites. This is in line with the review by [40], who found that students in Pasir Gudang, Malaysia, developed signs and symptoms of respiratory disease due to chemical poisoning from hazardous waste illegally disposed of into the Kim Kim River. Moreover, age influences dumping behaviour; for instance, ref. [29] found that younger respondents in Sylhet City, Bangladesh, were more knowledgeable and more concerned about waste management than older respondents. Therefore, they tend to practice proper waste disposal and report instances of IWD. Nonetheless, age alone does not shape perceptions of IWD, as other factors also play roles. Studies by [41] in Jambi, Indonesia, and [42] in Gauteng, South Africa, found no link between age and environmental perceptions or attitudes.

Overall, the literature suggests that different age groups are unequally affected by the health effects of IWD; however, age should not be treated as an isolated factor, as it covaries with other determinants. Based on inconsistent findings regarding the influence of age on environmental risk perception, some studies report notable variations across age groups, while others report no significant differences. Accordingly, this study posits the following hypothesis:

H4. 

There is no significant difference among age groups in their perceived impacts of IWD in the Thabazimbi Local Municipality.

2.4. Income and IWD

Income has consistently been identified as a predictor of IWD, with individuals in low-income areas facing greater impacts from IWD than those in other income groups. The prevalence of IWD in these areas is due to high population density, which leads to significant waste generation and, in turn, widespread IWD, driven by ineffective waste removal services and insufficient enforcement of waste management laws [24,43]. Such instances have been reported globally; for instance, ref. [21] identified a significant positive relationship between low-income areas and street waste dumping across four provinces in South Africa, attributing it to inadequate waste removal services. Similarly, ref. [44] in Brussels, Belgium, and [45] in Lagos, Nigeria, observed that IWD is prevalent in low-income neighborhoods due to a lack of enforcement and residents’ inability to afford private waste removal services. Conversely, in middle- to high-income areas, IWD is limited due to frequent municipal waste collection and the use of private waste collection services when gaps occur. These instances have been documented in a comprehensive analysis by [46] in England, which found that high-income areas are more likely to use government-licensed waste treatment options because they can afford the service and are more aware of the consequences of participating in IWD.

This body of literature suggests that IWD is merely a behavioural issue rather than a socio-economic issue, as income classes are strongly tied to environmental and health vulnerability, and residents in low-income areas are more susceptible. This vulnerability arises from institutional contrasts, such as a lack of waste infrastructure and inadequate waste removal services. This aligns with the findings of [47], who found that solid waste management services are inequitably distributed, disadvantaging low-income residents. Based on inconsistent findings regarding the influence of income on environmental risk perception, some studies report notable variations across income groups, while others report no significant differences. Accordingly, this study posits the following hypothesis:

H5. 

There is a significant difference among income groups in their perceived impact of IWD in the Thabazimbi municipality.

3. Materials and Methods

3.1. Study Area

This study evaluates the environmental, health, and socio-economic impacts of IWD and assesses how socio-demographic variables influence perceptions of these impacts in the Thabazimbi Local Municipality, South Africa (Figure 1). The municipality is one of the five local municipalities within the Waterberg District Municipality in Limpopo province. It includes semi-urban areas, mining towns, and rural settlements with a population of 65,047, comprising 26,832 households, of which 83.6% are formal dwellings [48]. The municipality’s economy is driven by mining, agriculture, and tourism, which pose several challenges typical of such regions, including waste management issues, pressure on municipal infrastructure, and uneven service delivery [49].

3.2. Sampling Procedure and Data Collection

The study adopted a quantitative research approach, which relies on numerical data and statistical techniques, thereby reducing researcher bias and enabling generalisation of the study results [50]. Participants aged ≥18 years were selected using a stratified random sampling approach. The municipality was first divided into six main areas [48,51], which served as strata to ensure representation across the entire study area. Within each area, participants were selected by random sampling, ensuring individuals had an equal chance of being included in the study. Proportional allocation was used to ensure the number of selected participants matched the population size, minimizing sampling bias and enhancing representativeness. This approach ensures that the sample is more likely to reflect the population’s actual characteristics; therefore, the findings can be generalised with a known confidence level [52]. The sample size was determined using Slovin’s formula, commonly used in survey research when the total population is known but information on population variability is limited [53]. Although alternative methods, such as Cochran’s and power analysis-based calculations exist, Slovin’s formula was selected due to its simplicity and suitability for large population surveys with limited prior variability data [53]. Using this approach, a sample size of 381 was determined from a population of 65,047, with a 5% margin of error at a 95% confidence level.

$$n={\displaystyle \frac{N}{(1+N{e}^2)}}$$

(1)

where $n$ represents the sample size, $N$ is the total population and $e$ is the margin of error.

Primary and secondary data were collected for the study, with primary data gathered from participants using a questionnaire. The questionnaire was developed based on an extensive review of relevant literature. ensuring that the items were grounded in established theoretical frameworks and empirical findings. The questionnaire was divided into four sections: (a) socio-demographic characteristics, (b) environmental impact indicators, (c) health impact indicators, and (d) socio-economic impact indicators. These indicators were measured using a multiple-choice Likert scale ranging from “strongly disagree” to “strongly agree,” with respondents indicating their level of agreement. The dependent variables for the study are environmental, health, and socio-economic impacts, while the independent variables are gender, education, age, and income. The questionnaire was initially piloted with 10 people to assess the validity of each question, and minor revisions were made to improve the wording and sequencing before administering it to the target sample. The final set of questionnaires was self-administered to the participants.

3.3. Ethical Consideration

Ethical clearance was obtained from the Humanities and Social Sciences Research Ethics Committee (HSSREC) of the University of KwaZulu-Natal (protocol reference number HSSREC/00007886/2024), and the study was conducted in compliance with the principles outlined in the Declaration of Helsinki. Formal permission was obtained from the Thabazimbi Local Municipality, which served as the primary institutional gatekeeper for administering questionnaires to residents within its jurisdiction. Participation in the study was entirely voluntary, and no individual was pressured or influenced to participate. Prior to administering the questionnaire, written consent was obtained from participants, allowing the researcher to use the information gathered from their responses.

3.4. Data Analysis

Data was coded and analysed using the Statistical Package for the Social Sciences (SPSS) version 30, a widely used software package that provides robust, comprehensive tools for quantitative analysis [54]. The study utilised Likert-scale data, which are inherently ordinal and do not meet the assumptions of continuous interval-level measurement. Prior to analysis, the dataset underwent thorough data cleaning and preprocessing to enhance the accuracy, reliability, and reproducibility of the results. Missing data, which accounted for less than 5% of responses and involved continuous variables, were imputed using the mean. Outliers were detected using the three-sigma (3σ) rule, with observations beyond ±3 standard deviations from the mean flagged as extreme cases. Each flagged observation was carefully reviewed to distinguish between data entry errors and legitimate outliers. Data entry errors were corrected, while valid but extreme values were retained to maintain the dataset’s integrity and capture the full range of perceptions. This method minimised potential bias and preserved statistical power by avoiding unnecessary data loss [55]. Descriptive statistics, including frequencies and percentages, were then computed to summarise participants’ demographic characteristics and the environmental, health, and socio-economic impacts of IWD.

3.4.1. Reliability Analysis

Reliability analysis of the Likert items on the questionnaire was conducted using Cronbach’s Alpha in SPSS. According to Tavakol et al. [54], Cronbach’s alpha measures the internal consistency of a set of survey items to determine whether the items consistently measure the same characteristics. Cronbach’s alpha reliability analysis results indicated that the three scales for the indicators demonstrated good internal consistency, with α = 0.7 for environmental impact, α = 0.9 for health impact, and α = 0.9 for socio-economic impact.

3.4.2. Inferential Statistical Analysis

Normality tests (Kolmogorov-Smirnov and Shapiro-Wilk) indicated that the data are not normally distributed; therefore, non-parametric tests were used. The Mann–Whitney test was used to compare perceived IWD impacts across gender, and the Kruskal–Wallis test was used to compare them across education, age, and income groups. All statistical tests were conducted at a 95% confidence level, with a statistical significance level set at p < 0.05. When significant, pairwise comparisons were conducted using Dunn’s test with Bonferroni correction to identify specific differences with adjusted p-values. Lastly, a Spearman’s rho correlation analysis was conducted to examine relationships between socio-demographic variables and perceived IWD impacts.

4. Results

4.1. Socio-Demographic Characteristics

The socio-demographic characteristics of the respondents are shown in

Table 1. Socio-demographic characteristics of the respondents.

Variable	Options	Frequency (N)	Percentage (%)
Gender	Female	193	51.9
	Male	184	48.1
Education	≤Grade 12	159	41.7
	Diploma	57	15.0
	Degree	22	5.8
	Postgraduate	21	5.5
	Other	122	32.0
Age	18–29	107	28.1
	30–39	124	32.5
	40–49	87	22.8
	50–59	43	11.3
	≥60	20	5.2
Income	≤R5000	245	64.3
	R5000–R10,000	68	17.8
	R10,000–R20,000	25	6.6
	>R20,000	43	11.3

. The participant composition of the study revealed a predominance of females, who represented 51.9% of the total sample. Given that there were only four respondents in the other category, the analysis primarily focused on the female and male participants, who collectively constituted the majority of the sample population. Approximately 41.7% of respondents had grade 12 or lower educational attainment, while 32% held other qualifications, such as adult education and training. The municipality has a predominantly young demographic, with most respondents aged 30–39 (32.5%), followed by 28.1% in the 18–29 age group and 22.8% in the 40–49 age group. Furthermore, 64.3% of 381 participants earn R5000 or less, reflecting a significant segment of the population in the low-income group in both municipalities.

4.2. Inferential Statistical Analysis

4.2.1. Pre-Analysis

Before conducting the primary analysis, the Kolmogorov-Smirnov and Shapiro-Wilk tests were run in SPSS to assess the normality of the data. The results (

Table 2. Normality test results for perceived impacts of IWD.

Impact Factors	Kolmogorov-Smirnova a			Shapiro-Wilk
	Statistic	df	Significance	Statistic	df	Significance
Environmental impacts of IWD	0.223	381	<0.001	0.849	381	<0.001
Health impacts of IWD	0.208	381	<0.001	0.856	381	<0.001
Socio-economic impacts of IWD	0.233	381	<0.001	0.836	381	<0.001

^a Lilliefors Significance Correction.

) revealed p-values < 0.05 for all three impact factors, suggesting that the data was not normally distributed; therefore, non-parametric tests were deemed suitable for analysis.

4.2.2. Environmental Impacts of IWD

Kruskal–Wallis and Mann–Whitney U tests were conducted to compare perceived environmental impacts across education, age, income, and gender groups using the impact statements (E1–E10) (

Table 3. Perceived environmental impacts and statistical differences across socio-demographic groups.

Code	Impact Statement	Agreement Level (%)					Kruskal-Wallis Test (p-Value)			Mann-Whitney U Test (p-Value)
		1	2	3	4	5	Education	Age	Income	Gender
E1	Affect plants and animals	2.1	2.4	5.2	78.7	11.5	0.959	0.794	0.023 *	0.630
E2	Reduce quality of life due to odour	2.6	1.3	3.9	73.2	18.9	0.968	0.824	0.001 **	0.166
E3	Releases toxic air pollutants	1.8	2.9	2.4	71.4	21.5	0.645	0.797	0.081	0.527
E4	Threat to soil quality	1.3	3.7	2.9	65.9	26.2	0.241	0.999	0.146	0.335
E5	Spread of invasive species	1.8	4.5	4.7	65.1	23.9	0.323	0.629	0.748	0.363
E6	Flooding due to the blockage of storm drainage	1.0	3.7	3.9	65.4	26.0	0.174	0.610	0.436	0.178
E7	Increase pests or rodents	1.8	1.8	3.4	66.4	26.5	0.302	0.999	0.496	0.663
E8	Lead to wildfires	0.5	2.4	3.9	65.4	27.8	0.394	0.355	0.802	0.285
E9	Threat to water quality	1.8	3.4	2.4	65.4	27.0	0.320	0.814	0.499	0.207
E10	Causes long-term environmental damage	2.4	2.1	3.4	64.3	27.8	0.168	0.643	0.444	0.465

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree. * Significant at p < 0.05. ** Significant at p < 0.01.

). The Kruskal–Wallis test results indicate no statistical differences in perceived impact across education or age groups, as all statements had p-values > 0.05. This is supported by the Likert-scale percentage distributions, which show consistently high levels of agreement across, with more than 80% of respondents either agreeing or strongly agreeing with the statements. However, the Kruskal–Wallis results indicate that income is a significant factor in two environmental impact statements (E1 and E2), with p-values of 0.023 and 0.001, respectively. These results indicate that the two perceived impacts vary significantly across different income groups. Moreover, the Mann–Whitney U test was conducted to assess whether there was a significant difference in perceived environmental impacts between males and females. The results revealed no significant differences, as p-values were greater than 0.05 across all statements. This is supported by the high level of agreement regarding the perceived impacts, with more than 80% of respondents agreeing or strongly agreeing. Overall, the findings indicate limited socio-demographic variation across the ten environmental impact statements, and the high level of agreement across groups highlights the strong collective recognition of the environmental risks associated with IWD.

Given the significant Kruskal–Wallis results for perceived environmental impacts (E1 and E2) across income groups, pairwise comparisons were conducted using the adjusted p-values (

Table 4. Pairwise Comparisons across income groups.

Impact Statement	Comparison	Mean Difference	Std. Error	Test Statistic	Sig. (p)	Adjusted Sig.
Affect plants and animals across household income	≤R5000–>R20,000	−9.562	14.404	−0.664	0.507	1.000
	≤R5000–R5000–R10,000	−19.960	11.941	−1.672	0.095	0.568
	≤R5000–R10,000–R20,000	−51.244	18.291	−2.802	0.005	0.031 *
	>R20,000–R5000–R10,000	10.398	16.974	0.613	0.540	1.000
	>R20,000–R10,000–R20,000	41.682	21.911	1.902	0.057	0.343
	R5000–R10,000–R10,000–R20,000	−31.284	20.376	−1.535	0.125	0.748
Reduce quality of life due to odour across household income	≤R5000–>R20,000	−5.582	15.247	−0.366	0.714	1.000
	≤R5000–R5000–R10,000	−15.745	12.639	−1.246	0.213	1.000
	≤R5000–R10,000–R20,000	−76.585	19.361	−3.956	<0.001	0.000 **
	>R20,000–R5000–R10,000	10.163	17.967	0.566	0.572	1.000
	>R20,000–R10,000–R20,000	71.004	23.192	3.062	0.002	0.013 *
	R5000–R10,000–R10,000–R20,000	−60.841	21.568	−2.821	0.005	0.029 *

* Significant at p < 0.05. ** Significant at p < 0.01.

). This was conducted using Dunn’s test with the Bonferroni correction. For E1, the results indicated significant differences only between the ≤R5000 and R10,000–R20,000 groups (p = 0.031). Therefore, this indicates that differences in perception were specific to the lowest and mid-upper income groups. For E2, significant pairwise differences found between three income groups ≤R5000 and R10,000–R20,000 (p < 0.001); >R20,000 and R10,000–R20,000 (p = 0.013); and R5000–R10,000 and R10,000–R20,000 (p = 0.029).

4.2.3. Health Impacts of IWD

Table 5. Perceived health impacts and statistical differences across socio-demographic groups.

Code	Impact Statement	Agreement Level (&)					Kruskal-Wallis Test (p-Value)			Mann-Whitney U Test (p-Value)
		1	2	3	4	5	Education	Age	Income	Gender
H1	Lead to waterborne diseases such as cholera.	2.1	1.8	6.3	66.9	22.8	0.764	0.938	0.379	0.282
H2	Causes respiratory problems such as asthma.	0.5	1.1	2.9	66.8	28.7	0.848	0.897	0.463	0.841
H3	Causes skin irritation, rashes, and burns when community members come into direct contact with toxic waste from the dumps.	0.3	1.3	3.7	64.3	30.4	0.653	0.390	0.675	0.230
H4	Cause water pollution can cause diarrhea.	0.3	0.8	4.5	60.9	33.6	0.732	0.129	0.343	0.947
H5	Lead to stress, anxiety, and depression for community members living near waste dumps.	1.0	2.1	5.2	58.5	33.1	0.308	0.534	0.188	0.633
H6	It harms children due to their developing bodies.	0.5	1.6	4.5	58.0	35.4	0.687	0.406	0.273	0.535
H7	Harms public health.	0.5	1.0	1.8	58.3	38.3	0.812	0.212	0.468	0.390
H8	Increases healthcare costs.	0.5	2.4	3.7	58.3	35.2	0.745	0.383	0.359	0.353
H9	It can be a breeding ground for mosquitoes, which can lead to malaria.	0.5	1.3	2.9	65.9	29.4	0.931	0.365	0.612	0.838
H10	Contain sharp objects that can harm people, especially children playing near illegal dumping sites.	0.8	1.8	2.6	64.6	30.2	0.087	0.466	0.986	0.745

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree.

presents the Kruskal–Wallis and Mann–Whitney test results for perceived health impacts (H1–H10) by assessing whether there are significant differences across education, age, income, and gender groups. The results of both tests indicated no significant differences across all socio-demographic groups (p > 0.05). These results indicate that respondents’ perceptions were consistent across demographic groups. This is supported by the high level of agreement, with more than 90% of respondents agreeing or strongly agreeing that IWD has health impacts.

4.2.4. Socio-Economic Impacts of IWD

Kruskal–Wallis and Mann–Whitney tests were conducted to compare perceived socio-economic impacts based on the impact statements (S1–S10) across education, age, income, and gender groups (

Table 6. Perceived socio-economic impacts and statistical differences across socio-demographic groups.

Code	Impact Statement	Agreement Level (%)					Kruskal-Wallis Test (p-Value)			Mann-Whitney U Test (p-Value)
		1	2	3	4	5	Education	Age	Income	Gender
S1	Makes communities visually unappealing, leading to property devaluation.	1.0	2.6	4.7	70.3	21.3	0.585	0.244	0.887	0.572
S2	Reduces economic opportunities and growth.	1.0	1.6	3.1	68.2	26.0	0.073	0.961	0.753	0.533
S3	Increases the community’s crime rate.	0.8	2.6	2.9	63.8	29.9	0.325	0.727	0.820	0.825
S4	Impacts reduce the sense of community.	1.0	2.4	2.4	60.9	33.3	0.105	0.775	0.231	0.778
S5	Clean-up costs the municipality more money.	0.8	2.1	2.9	59.8	34.4	0.044 *	0.495	0.221	0.494
S6	Causes causing long-term financial losses.	1.0	1.8	2.9	58.5	35.7	0.050 *	0.369	0.950	0.889
S7	Negatively affects the tourism sector in the area.	0.3	1.0	2.9	62.2	33.6	0.624	0.205	0.649	0.945
S8	Increases vagrancy in the community.	0.5	2.1	2.4	63.3	31.8	0.577	0.391	0.821	0.383
S9	Affects low-income communities.	2.1	0.8	1.3	61.9	33.9	0.557	0.137	0.793	0.195
S10	Causes stigma and shame to the affected community.	1.0	1.0	3.1	61.4	33.3	0.738	0.285	0.551	0.510

1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree. * Significant at p < 0.05.

). The Kruskal–Wallis test results revealed no significant differences in perceived impact across age or income groups, as all statements had p-values > 0.05. This is supported by Likert-scale distributions, which show consistently high levels of agreement across all statements, with more than 90% of respondents agreeing or strongly agreeing. In contrast, the results revealed that education is a significant factor in two socio-economic impact statements (S5 and S6), with p-values of 0.04 and 0.05, respectively. These results indicate that the two perceived impacts vary significantly across income groups.

Given the significant Kruskal–Wallis results for perceived socio-economic impacts (S5 and S6) across income groups, Dunn’s test with the Bonferroni correction was used to conduct pairwise comparisons with adjusted p-values (

Table 7. Pairwise Comparisons across education groups.

Impact Statement	Sample 1–Sample 2	Mean Difference	Std. Error	Test Statistic	Sig. (p)	Adjusted Sig.
Clean-up costs the municipality more money.	Postgraduate–Diploma	33.114	24.265	1.365	0.172	1.000
	Postgraduate–Other	−42.456	22.458	−1.890	0.059	0.587
	Postgraduate–Degree	57.606	29.000	1.986	0.047	0.470
	Postgraduate–≤Grade 12	59.805	22.071	2.710	0.007	0.067
	Diploma–Other	−9.342	15.251	−0.613	0.540	1.000
	Diploma–Degree	−24.492	23.859	−1.027	0.305	1.000
	Diploma–≤Grade 12	26.691	14.675	1.819	0.069	0.689
	Others–Degree	15.150	22.018	0.688	0.491	1.000
	Others–≤Grade 12	17.349	11.441	1.516	0.129	1.000
	Degree–≤Grade 12	2.199	21.623	0.102	0.919	1.000
Causes causing long-term financial losses.	Postgraduate–Diploma	37.714	24.411	1.545	0.122	1.000
	Postgraduate–Other	−39.143	22.592	−1.733	0.083	0.832
	Postgraduate–Degree	54.654	29.174	1.873	0.061	0.610
	Postgraduate–≤Grade 12	60.022	22.203	2.703	0.007	0.069
	Diploma–Other	−1.429	15,342	−0.093	0.926	1.000
	Diploma–Degree	−16.939	24.002	−0.706	0.480	1.000
	Diploma–≤Grade 12	22.308	14.763	1.511	0.131	1.000
	Other–Degree	15.510	22.150	0.700	0.484	1.000
	Other–≤Grade 12	20.879	11.510	1.814	0.070	0.697
	Degree–≤Grade 12	5.369	21.753	0.247	0.805	1.000

). The results revealed that, despite significant differences between the ≤grade 12 and postgraduate (p = 0.007) and degree and postgraduate (p = 0.047) groups, after applying the Bonferroni correction, there was no significant difference among the educational groups (p > 0.05).

Overall, the results indicate that, among the four socio-demographic variables, a statistical difference in perceived impacts was observed between the income and education groups; therefore, H5 is supported, while H3 is not. Furthermore, the results revealed no significant differences in perceived impacts across age groups and genders; therefore, H2 and H4 were supported. Moreover, the high agreement on perceived impacts indicates that IWD is a universal challenge that affects people across all socio-demographic groups in the municipality.

4.2.5. Correlation Analysis

Spearman’s rho correlation analysis was conducted to assess the relationships between socio-demographic variables (gender, education, age, and income) and perceived environmental, health, and socio-economic impacts of IWD. The analysis results (

Table 8. Spearman’s rho correlations between socio-demographic variables and perceived impacts of IWD.

Variable	1	2	3	4	5	6	7
1. Gender	—	0.138 **	0.167 **	0.076	0.071	−0.003	0.035
2. Education		—	0.220 **	0.059	−0.087	−0.055	−0.065
3. Age			—	0.210 **	0.047	−0.003	0.000
4. Income				—	0.068	0.096	−0.001
5. Environmental impacts					—	0.500 **	0.484 **
6. Health impacts						—	0.391 **
7. Socio-economic impacts							—

** Correlation is significant at the 0.01 level (2-tailed).

) indicate no significant association between the socio-demographic variables and the perceived impacts of IWD (p > 0.05); therefore, H1 is not supported. However, a strong positive relationship was observed among the three impact factors, indicating that respondents who recognised one of the impact factors were likely to share similar perceptions of the other impact factors. Moreover, a weak positive relationship was observed among the socio-demographic variables, suggesting limited interdependence.

5. Discussion

This study examined residents’ perceptions of the environmental, health, and socio-economic impacts of IWD in the Thabazimbi Local Municipality and identified notable differences across socio-demographic groups. The overall findings revealed strong agreement in the perceived impacts of IWD, and the statistical results showed significant differences in perceived environmental and socio-demographic impacts across education and income groups. Moreover, the correlation analysis revealed no relationship between the socio-demographic variables and perceived impacts. This study’s significance lies in strengthening the empirical evidence of perception studies by statistically analysing the relationships between socio-demographic variables and the perceived impacts of IWD. Nonetheless, certain limitations must be acknowledged. First, the study’s focus on the Thabazimbi Local Municipality restricts the generalizability of the findings to other municipalities with different demographic and economic contexts. Consequently, the conclusion that certain factors significantly influence community perceptions may not hold for municipalities with varying levels of infrastructure, enforcement capacity, or public awareness. Second, the analysis used a limited range of socio-demographic variables (gender, age, education, and income) to assess significant differences in perceptions of IWD. This exclusion of potentially influential factors, such as household size and proximity to dumping sites, may introduce omitted-variable bias, potentially affecting the strength and direction of the observed relationships. While this study provides valuable insights into community perceptions and significant correlations with IWD within the municipality, the conclusions should be interpreted with caution, as they may be context specific and influenced by unobserved variables.

5.1. Perceptions of Environmental Impacts

The study found strong agreement among respondents regarding the environmental impacts of IWD across all impact statements, with more than 80% agreeing or strongly agreeing with the statements. This indicates that most residents experience detrimental impacts and reflects a strong collective awareness of environmental risks. The widely perceived environmental impacts of IWD were that IWD led to wildfires, posed threats to soil and water quality, and caused long-term environmental damage. These findings align with those of [56] in Delta State, Nigeria; [2] in Joe Slovo Park, South Africa; and [57] in Harare, Zimbabwe, who found that IWD has significant negative impacts on environmental degradation through soil and water pollution, blocked drainage systems leading to flooding, and foul odors that lower the quality of life. These findings suggest that IWD occurs across countries, indicating that it is not only a context-specific challenge but also a regional one. Furthermore, the current research’s statistical analysis revealed significant differences in the perceived environmental impacts of IWD among various income groups. These differences can be attributed to factors such as access to waste management services and socio-economic vulnerability. For instance, residents in low-income households are often disproportionately affected by IWD due to inadequate municipal waste management practices, in contrast to their middle and high-income counterparts. As a result, individuals in lower socio-economic strata tend to recognize and describe the impacts of waste disposal as more severe. This observation aligns with the findings of [1] in Komani, South Africa, and [44] in Brussels, Belgium, which indicate that IWD predominantly affects low-income households, leading to perceptions of immediate and severe consequences due to proximity to waste dumps. Conversely, residents in middle to high-income households exhibit distinct perceptions of IWD, primarily owing to their limited exposure to waste dumps, facilitated by the ability to engage private waste removal services when public provision falls short. This disparity is also reflected in studies conducted by Musasa et al. [31] in Chegutu, Zimbabwe, and Yukalang et al. [58] in Thailand. Overall, the findings indicate that perceptions of IWD differ across income groups, underscoring the influence of exposure to waste dumps and residents’ socio-economic status.

Despite significant differences among income groups, only two environmental impact statements (E1 and E2) showed significant variability, suggesting a general uniformity across the other statements. Pairwise comparisons indicated a significant difference in E1 between the lowest- and middle-income groups, whereas E2 showed a significant difference primarily within the middle-income group. Therefore, perceptions of environmental impacts are not linear, with marked differences evident in specific income segments. Furthermore, the absence of significant differences across gender, age, and education suggests that the impacts of IWD are universally experienced by residents, regardless of these demographic factors. This finding is consistent with studies conducted by [42] in Gauteng, South Africa; Ref. [59] in Europe; and [60] in South Africa, which reported no significant differences in environmental concern or risk perceptions based on gender, age, or education. In contrast, research by Alam and Zakaria [29] in Sylhet, Bangladesh, and [61] in Gipuzkoa, Spain, indicates that perceptions are significantly shaped by gender, age, and education. These differing conclusions highlight that the influence of socio-demographic variables on perceptions may be context specific, varying across geographic and socio-economic landscapes.

5.2. Perceptions of Health Impacts

The study revealed that more than 85% of respondents perceived that IWD has health impacts on surrounding communities, strongly associating it with respiratory illnesses, skin conditions, vector-borne diseases, and increased healthcare costs. These findings are consistent with cases in the Ashanti Region of Ghana, where Peprah et al. [62] found that the majority of respondents reported experiencing various health symptoms, including sleep problems, extreme tiredness, stress, anxiety, and depression, due to residing in proximity to the dumping sites. Moreover, a comparable study by Akmal and Jamil [63] found that individuals residing within 100 m of an IWD site in Pakistan were at a heightened risk of malaria, dengue fever, and asthma compared to those living more than 500 m away. The strong agreement reflects strong community awareness of the public health effects of IWD within the municipality. Statistical analysis revealed no significant differences in perceived health impacts across the various socio-demographic variables. Therefore, suggesting that members of the community experience the health effects of IWD irrespective of their gender, age, income, or educational attainment. This is consistent with the findings of [23,33], who reported no significant difference in awareness of the risks posed by IWD.

However, several empirical studies report contradictory findings, indicating that health impacts are often unevenly experienced across groups. For instance, Peprah et al. [61] found statistically significant differences in health outcomes across gender, age, and income groups living near landfill sites. This implies that exposure to health impacts shapes residents’ perceptions. This is in line with a study by Raphela et al. [19] in KwaZulu-Natal, South Africa, which found that residents of low-income households have different perceptions due to their prolonged exposure to the IWD compared to residents of other income groups. Overall, the findings indicate that, despite strong agreement on perceived health impacts associated with IWD across all socio-demographic groups, underlying inequalities in exposure and vulnerability may result in disproportionately greater health impacts among certain groups.

5.3. Perceptions of Socio-Economic Impacts

The study revealed that more than 90% of respondents perceived that IWD had socio-economic impacts in their communities. The community perceived that IWD results in high clean-up costs, causes long-term financial losses, impacts the tourism sector, and reduces the economic value of the communities. These results are in line with the findings of [1,64,65], who found that IWD not only costs municipalities millions to clean up but also affects the tourism sector and reduces the area’s economic value. These findings suggest that IWD undermines local economic development and public finances. On the other hand, the statistical analysis revealed significant differences in the perceived socio-economic impacts of IWD across educational groups. This finding is supported by studies by [66,67], which found that education influences individuals’ perceptions and attitudes towards environmental issues. However, further analysis revealed that these differences were minimal; none of the educational groups showed differences after applying the Bonferroni correction. These results suggest that while education may influence perceived impacts, the effect is weak and not statistically robust, highlighting broadly similar perceptions across educational groups. This is consistent with the study’s conclusions that no significant differences in perceived socio-economic impacts exist across age, gender, and income groups. Overall, the findings suggest a shared community experience, reinforcing the need for generalized interventions rather than those segmented by demographic factors.

The main hypothesis of this study posited a statistically significant relationship between socio-demographic variables (gender, education, age, and income) and the perceived environmental, health, and socio-economic impacts of IWD. However, the study’s results did not support this hypothesis, indicating that no statistically significant relationship was found. The rejection of the hypothesis implies that socio-demographic variables do not significantly influence perceptions of IWD impacts. This finding aligns with the results of studies [23,33], which also found no significant differences in the impacts of IWD and socio-demographic variables. Conversely, empirical evidence suggests that socio-demographic variables can influence perceptions of environmental issues. For example, research conducted by [42] in Gauteng, South Africa, and [68] in Albania demonstrated that variables such as age, education, and gender do affect perceptions regarding environmental concerns. Overall, these findings indicate that IWD should be understood in a context-specific manner, as the influence of socio-demographic variables may vary depending on the type of environmental issue, the effectiveness of local governance, and the degree of environmental exposure. Furthermore, the analysis uncovered a correlation among the environmental, health, and socio-economic impacts, suggesting that individuals perceive the effects of IWD as interconnected challenges. This implies that awareness of one impact factor increases the likelihood of awareness of the others [69]. The results highlight the context-specific nature of perception formation regarding IWD, while also emphasising the interrelatedness of the impacts, underscoring the necessity of holistic and inclusive environmental management strategies.

6. Conclusions

This study explored residents’ perceptions of the environmental, health, and socio-economic impacts of IWD within the Thabazimbi Local Municipality and identified notable differences across socio-demographic groups in these perceptions. The research utilised non-parametric methods, including the Kruskal-Wallis and Mann-Whitney U tests, as well as Spearman’s rho correlation, to examine the relationship between perceived impacts and socio-demographic variables. The findings revealed a strong consensus on the perceived impacts of IWD, indicating that it is a widespread issue affecting communities throughout the municipality. Moreover, the statistical analysis showed that perceptions of environmental and socio-economic impacts varied among different income and education groups; however, these differences were minimal across the various impact statements. This aligned with the correlation results, which indicated no significant relationship between socio-demographic variables and perceived impacts.

In light of the study’s findings, several recommendations are made to mitigate the impacts of IWD. First, the municipal waste management system should be strengthened by expanding waste collection services to all areas of the municipality, particularly informal settlements, where high waste generation coincides with inadequate waste removal services. Additionally, enforcement of existing waste management bylaws should be improved by providing essential infrastructure, such as bins and refuse bags, and conducting regular inspections throughout the municipality to ensure compliance. Second, awareness campaigns should be launched to educate communities about the detrimental effects of IWD practices. This can be achieved through various communication channels, including workshops, social media, local newspapers, school programs, and the distribution of informative pamphlets and posters to reach all demographic groups within the municipality. Finally, integrating clean-up initiatives and establishing community recycling projects will provide practical approaches to encourage behavioural change. Addressing IWD within the municipality is vital not only for achieving sustainable waste management and advancing the Sustainable Development Goals, but also for protecting the environment and promoting public health.