An Assessment of the Knowledge, Attitudes, and Practices Toward General Waste Segregation among the Population of the United Arab Emirates

Abstract

Increases in the human population and economic development have led to a rise in waste production, which poses significant environmental risks and presents a pressing global issue in waste management. Among other countries, this situation affects the United Arab Emirates (UAE). On the other hand, poor waste segregation practices can result in failed waste recycling efforts, leading to the excessive use of resources and worsening issues (such as energy consumption, global warming, and sustainable development). Waste segregation is a crucial step in waste management, which involves dividing waste according to its characteristics and type. By following this procedure, recycling effectiveness is increased, the environmental impact is decreased, and hazardous material disposal is ensured. Beneficial waste segregation reduces contamination, making it possible to recover valuable materials and thus use fewer landfills. Even though the failings in waste segregation are a severe issue, insufficient research has been carried out. This includes research on the knowledge, attitudes, and practices (KAP) of people living in the UAE regarding waste segregation, information which is crucial to developing a successful intervention to address this problem. The current study evaluated the KAP concerning waste segregation among UAE citizens and identified correlations between KAP variables, with the primary aim of filling a research gap, while analyzing the correlations between sociodemographic characteristics and KAP levels, which was the secondary aim. This was accomplished by a cross-sectional study conducted all over the UAE. Data were collected from 391 participants using a five-point Likert scale questionnaire that was developed from previous research and investigated sociodemographic characteristics, waste segregation practices (5), attitudes (5), and knowledge (5). UAE University’s ethical committees approved this study (ERSC_2024_4360) for research, and the results were confirmed through statistical analyses and Cronbach’s alpha testing. The inclusion criteria targeted residents of the UAE who were 18 years of age or above, and the survey was distributed via an online platform (Google Forms) with non-probability sampling. G*Power statistical power analysis estimated a minimum sample size of 385 participants. To identify correlations in the results, a structural equation model (SEM) and SPSS, such as Chi-square tests and Spearman correlation coefficients, were used to assess the associations between KAP variables. These tests were chosen for their robustness in handling categorical and continuous data, respectively. A notable majority (84.1%) of the participants were female, and 15.9% were male; the gender difference was highly significant, as revealed by the Chi-square test. Most participants (67.0%) fell into the 18–24 age group. The highest level of education reported was a bachelor’s degree (47.3%). The parents’ educational levels showed a relatively high level of education, with more than half having at least a high school degree or higher: father’s education level (67.2%) and mother’s education level (73.1%). Most participants were students (58.8%), but a significant portion of the sample was employed (25.1%). Unemployment was reported at 12.3%. The parents’ employment statuses showed a higher percentage of unemployed mothers (49.4%) compared to fathers (6.9%). The average scores suggested a favorable inclination toward sustainability (mean ± standard deviation (SD) for knowledge, 3.59 ± 0.78; poor knowledge, 3.6%; and excellent knowledge, 16.9%), attitudes (3.73 ± 0.77; poor attitudes, 2.8%; and excellent attitudes, 22.5%), and practices (3.62 ± 0.76; poor practices, 2.3%; and excellent practices, 16.4%), with all the means surpassing the midpoint. In the correlation test, the current study demonstrated positive correlations between knowledge and attitudes (r = 0.666, p < 0.001) and between knowledge and practices (r = 0.682, p < 0.001). Also, a positive correlation (r = 0.159, p < 0.001) was found between general waste segregation KAP and sociodemographic variables, with a significant correlation (r = 0.110) between attitudes and gender. These findings emphasize the possibility of using focused educational and policy interventions to improve waste segregation behaviors. An additional investigation is advised to delve into the fundamental mechanisms behind these correlations and devise customized approaches to encourage sustainable waste management practices among various demographic groups in the UAE.

1. Introduction

The increase in the human population and economic development have led to a rise in waste production, with 2 billion tons of total waste produced in 2016 [1], which has risen to 7–9 billion tons of waste produced today [1,2]. This poses significant environmental risks and presents a pressing global issue in waste management [3,4]. On the other hand, poor waste segregation practices can result in failed waste recycling efforts [5,6]. These may arise due to a deficiency of modernized methods, techniques, and systems or a slow change from outdated waste management models [6]. Furthermore, inadequate infrastructure, limited public awareness, insufficiently feasible projects, and public policies that fail to achieve the intended purpose have been identified as contributing factors [7].

Poor waste management methods were attributed to insufficient training for staff, resource shortening, and inadequate budget support [8], leading to the excessive use of resources and worsening issues such as energy consumption, global warming, and sustainable development [9]. Therefore, effective interventions are necessary to address these challenges [10]. In 2016, the United Nations adopted 17 Sustainable Development Goals (SDGs) [11], with 8 directly or indirectly related to waste management [12]. This highlights the importance of addressing waste challenges comprehensively [13]. The first step in comprehensive waste management is understanding the general waste segregation procedure and what affects this procedure [14].

General waste segregation is a method that promotes circular economy principles and ecological conservation as economies expand [15]. The process involves separating reusable and recyclable materials from the general waste [16]. These practices involve classifying waste into distinct groups: paper, plastic, glass, metal, etc. [17]. Additionally, municipal solid waste (MSW) can be categorized into seven primary groups: organics, paper/boards, plastics, glass, metals, textiles, and inert materials [17]. Then, organic waste is sorted into several categories, such as garden trash, animal by-products, and dairy waste, to be transformed into bioenergy and organic fertilizer [16,18,19]. Doing so can conserve landfill space, minimize environmental and public health impacts, and make resources more easily accessible through the recycling field [6,16]. However, it is a complex procedure requiring careful attention to detail and specific techniques to ensure optimal effectiveness and achieve the desired outcomes [10,15].

In addition, knowledge, attitudes, and practices (KAP), concerning waste segregation can impact the effectiveness of waste segregation, as has been studied in several countries, such as China, where researchers found that knowledge of waste segregation significantly influences household waste sorting practices [10]. According to the study’s results, practices and knowledge have a significant positive relationship (p < 0.01). Additionally, a high level of knowledge and positive attitudes regarding waste sorting will improve practices in the field [10].

On the other hand, at Hamadan University in Iran [4], a positive correlation was observed between attitude and knowledge, as well as practices. Their study highlights the interplay between KAP, suggesting that a positive attitude toward waste management is associated with more excellent knowledge and better waste sorting practices. Additionally, knowledge levels influence waste sorting performance [4].

Similarly, Malaysia emphasizes the significant impact of attitude on waste separation behavior among households [20]. Kasmuri et al. [20] suggest that a favorable attitude significantly affects waste separation behavior. This underscores the importance of promoting positive attitudes toward waste management [20]. In South Africa, gender was identified as a significant factor influencing waste separation practices [21]. Roos et al. [21] reported a statistically significant relationship between gender and waste separation behavior (p = 0.02) [21].

The study of KAP research on waste segregation in the United Arab Emirates (UAE) sheds light on a serious part of the environment and sustainable economic development [22,23]. Waste management is a pressing global issue in the UAE [23]. Understanding the KAP of the population toward waste segregation is essential for designing effective policies and initiatives to address this issue and try to minimize it [24,25], which will close the economic wasting loop that results from wasting resources [26,27]. On the other hand, SDG can be achieved by implementing the KAP results on the development plan for achieving the SDG [28,29,30,31].

Moreover, in the UAE, one study examined the impact of age on residents’ knowledge of waste management practices. Contrary to expectations, they found no significant difference in knowledge levels across age groups (p > 0.05). This suggests that age may not be a substantial determinant of waste management knowledge in the UAE, emphasizing the need for broader educational initiatives targeting diverse demographic groups [32].

In addition, Dubai in the UAE mostly employs small scale recycling initiatives to handle materials such as steel, plastic, and wood [33]. Furthermore, the city utilizes specialized landfills to manage construction and demolition (C & D) trash effectively. Dubai Municipality (DM) implements regulations for sustainable construction to reduce CO₂ emissions and energy consumption. These regulations mandate using environmentally friendly materials and thermal insulation [33]. To mitigate the adverse environmental impacts of cement factories, DM has also enforced the utilization of eco-friendly cementitious materials [33].

On the other hand, in Ajman, UAE, the emirate’s rapid population growth and industrial expansion have led to a significant increase in garbage output, posing a critical problem for waste management. The planning department and the Ajman municipality have initiated several projects to improve waste management protocols to effectively tackle these challenges [22]. These activities encompass the conversion of waste into energy, the treatment of wastewater, and the regulation of the transportation of hazardous waste. The municipality also prioritizes educating the general public about the significance of waste sorting and recycling [22]. Ajman has achieved notable advancements, such as reducing the quantity of garbage sent to landfills after implementing the waste incinerator [22].

The Environmental Agency Abu Dhabi (EAD) was established in 1996 to protect groundwater, biodiversity, air quality, and ecosystems in Abu Dhabi, UAE, and is responsible for waste management in the region [23]. The EAD aims to advance environmental consciousness, facilitate sustainable progress, and guarantee environmental durability in accordance with the UAE’s foremost objectives [23]. The tasks for waste management are distributed among three primary institutions: Tadweer oversees the management of municipal trash, commercial, medical, agricultural, industrial, and construction and demolition garbage [23]. The Abu Dhabi National Oil Company (ADNOC) is accountable for oil and gas waste, and the Federal Authority for Nuclear Regulation (FANR) is in charge of handling radioactive waste [23].

In addition, the Centre of Waste Management Abu Dhabi (CWM) and the Department of Municipal Affairs (DMA) provide support for waste reduction initiatives in Abu Dhabi [23]. Sharjah, UAE, employs an intricate waste management system that involves multiple steps and significant actors [34]. Bee’ah, an environmental company responsible for overseeing the collection, transportation, and processing of MSW, is tasked with managing the waste management plan of Sharjah [34]. Prior to being dispatched to a material recovery facility (MRF), refuse is gathered in compactors and conveyed to a transfer station, where it is temporarily held. Waste is placed on the tipping floor at the MRF and then transferred onto feeding conveyors for sorting [34]. This procedure aims to retrieve necessary secondary raw materials (SRM) by utilizing a combination of mechanical and manual sorting processes [34].

Despite the importance of understanding the KAP of waste segregation in improving both the environment and economy, there is a lack of research on the waste segregation of UAE population KAP regarding these matters. Accordingly, there is a need for more research studies to reduce this gap.

The current study aims to find the KAP regarding the waste segregation of individuals toward waste segregation in the UAE, thereby contributing to the development of sustainable waste segregation practices. The KAP of waste segregation research gave us a deep view of the current situation and it will help the country protect its environment and improve its economy by understanding the KAP of the population and taking action to improve it [35,36,37].

The current study aims to answer the following research questions:

RQ 1. What is the KAP level of the consequences of waste segregation in the UAE population, and how does it impact waste segregation in the UAE?

RQ 2. Is there any correlation between KAP and sociodemographic data in the UAE?

Similarly, the present study aims to verify the following research hypotheses:

H0. 

KAP and sociodemographic data do not impact waste segregation in the UAE.

H1. 

There is a correlation between waste segregation in the UAE and factors of KAP, and sociodemographic characteristics.

2. Materials and Methods

2.1. Study Design and Data Collection

Data were collected for the present descriptive analytical cross sectional study using a validated online questionnaire by UAEU Social Sciences Ethics Committee Research Number: ERSC_2024_4360, obtained on 9 March 2024. The questionnaire, built on previous articles, consisted of 15 items measured on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The sample population was determined using the G*Power version 3.1.9.6 program to be 385 [38], and the sample size was calculated based on the size of the UAE population [39].

The determined sample size was 385, as calculated by the G*Power program [38]. The online questionnaire was given randomly to 431 individuals. Participants willingly consented to complete it and had the option to withdraw from it at any point. The purpose of collecting these numbers was to prevent errors, and we achieved a response rate of 100%.

Subsequently, to ensure a broad representation of views on waste segregation, the inclusion standards of participants include volunteering and living in the UAE, and participants should be between 18 and 64 years old. Exclusion standards included participants less than 18 years old and visitors to UAE. The sample for this study was obtained via non probability sampling, resulting in a response rate comprising 84.1% females and 15.9% males. This demographic distribution supports our findings, indicating a correlation between waste segregation KAP and gender in the UAE. The overall Cronbach’s alpha coefficient for the KAP questions was 0.891, indicating acceptable internal consistency and reliability of the study instrument.

The first part of the survey questionnaire has 10 control questions that will be used to record sociodemographic data; the rest of the questionnaire includes 15 questions developed to capture waste segregation divided into 5 knowledge questions (

Table 1. Knowledge questions.

	Question	Reference
KQ 1	I know what waste segregation is.	[40]
KQ 2	I am aware of recycling or waste separation programs in the local municipality.	[21]
KQ 3	I know where to put my household waste after separation.	[41]
KQ 4	I am familiar with hazardous waste sorting.	[10]
KQ 5	I know what types of waste are recyclable.	[21]

), which included questions about familiarity with waste segregation and sorting [10,21,39,40,41]; 5 attitude questions (

Table 2. Attitude questions.

	Question	Reference
AQ 1	Waste sorting is effective in controlling environmental pollution.	[4]
AQ 2	I separate garbage by type and throw waste materials to the areas designed for them (glass, plastics, paper, organic or other) when it is available.	[21]
AQ 3	I think there is a relationship between education level and knowledge of waste management.	[4]
AQ 4	I think waste sorting can recycle resources.	[10]
AQ 5	Waste separation at my place is good for the environment.	[41]

) toward waste separation, sorting, and recycling will also be examined [4,10,41]. Furthermore, the other five practice questions (

Table 3. Practice questions.

	Question	Reference
PQ 1	I think it will do great harm to environment if I do not sort waste.	[10]
PQ 2	I recommend others to do waste sorting for recycling.	[4]
PQ 3	I use separate containers for waste sorting.	[4]
PQ 4	I think it will waste resources if I cannot sort waste properly.	[10]
PQ 5	I have practiced waste separation for some time.	[41]

) determinants, such as practice toward waste segregation, will be investigated [4,10,41].

2.2. Data Analysis Tools

The statistical analyses were carried out to evaluate and compare knowledge about plastics among participants. Data were collected, checked, revised, and organized in tables and figures using Microsoft Excel 2016. Normality was applied to check the data normality, whether parametric or nonparametric data, using the Shapiro–Wilk test and Kolmogorov–Smirnov at p = 0.05 level.

The nonparametric data were described statistically using both graphical and numerical descriptions. Parametric data were defined in terms of mean and standard deviation (SD). Nonparametric data were expressed in terms of frequency (n, %), mean, median, mode, quartiles (Q1, Q3), and SD. Inferential statistics in nonparametric data for comparing scores were performed using the Chi-square test for goodness-of-fit. However, inferential statistics of parametric data for comparing KAP were created using one way analysis of variance (ANOVA) at p = 0.05 level.

ANOVA was followed by Duncan’s Multiple Range Test (DMRTs) at p = 0.05 level. The questionnaire reliability and internal consistency were assessed using Cronbach’s alpha test at a probability level of 0.05. Three significance levels were proposed at p = 0.05 (*), at p = 0.01 (**), and p = 0.001 (***). Data analyses were conducted using the computer software Statistical Package for Social Science (SPSS), IBM-SPSS version. 29.0 for Mac OS [42]. The principal component analysis (PCA) was applied as a method for determining the weights in an index and identifying the underlying factors or components driving the variation in a dataset. PCA was performed using PAST statistical software version 4.0 [43].

3. Results

3.1. Sociodemographic Variables

The target sample size was 385. However, 431 were collected to avoid errors. Any respondents younger than 18 and outside the UAE were deleted. There were ten sociodemographic variables and 391 respondents in the UAE.

A notable majority (84.1%) of the participants were females, and (15.9%) were male; the gender difference was highly significant, as revealed by the Chi-square test. Most participants (67.0%) fall into the 18–24 age group; the representation decreased steadily in older age groups, with no participants over 65. The highest level of education reported was a Bachelor’s degree (47.3%), followed by a high school degree or equivalent (43.2%).

Only a tiny percentage completed a master’s (4.6%) or doctorate (1.8%). The parents’ educational levels showed a relatively high level of education, with more than half having at least a high school degree or higher. The father’s education level was found to be 67.2%, and the mother’s education level was found to be 73.1%.

Interestingly, the mother’s educational level was slightly higher on average than the father’s. Most participants were students (58.8%), with a significant portion employed (25.1%), and unemployment was found to be 2.3%. The parent’s employment status showed a high percentage of unemployed mothers (49.4%) compared to fathers (6.9%). In addition, a significant proportion of fathers were retired (25.3%), which was consistent with the age distribution of the sample (Figure 1) (

Table 4. Sociodemographic variables (N = 391).

Variables		Frequency		Chi-Square
		n	%
Gender	Male	62	15.9%	<0.001 ***
	Female	329	84.1%
Age	18–24 years old	262	67.0%	<0.001 ***
	25–34 years old	49	12.5%
	35–44 years old	62	15.9%
	45–54 years old	15	3.8%
	55–64 years old	3	0.8%
	Older than 65 years old	0	0%
The highest level/degree of education	Less than high school	12	3.1%	<0.001 ***
	High school degree or equivalent	169	43.2%
	Bachelor’s degree (e.g., BA, BS)	185	47.3%
	Master’s degree (e.g., MA, MS, MEd)	18	4.6%
	Doctorate (e.g., PhD, EdD)	7	1.8%
Mother’s education level	Less than high school	105	26.9%	<0.001 ***
	High school degree or equivalent	130	33.2%
	Bachelor’s degree (e.g., BA, BS)	136	34.8%
	Master’s degree (e.g., MA, MS, MEd)	15	3.8%
	Doctorate (e.g., PhD, EdD)	5	1.3%
Father’s education level	Less than high school	107	27.4%	<0.001 ***
	High school degree or equivalent	115	29.4%
	Bachelor’s degree (e.g., BA, BS)	99	25.3%
	Master’s degree (e.g., MA, MS, MEd)	49	12.5%
	Doctorate (e.g., PhD, EdD)	21	5.4%
Current employment status	Other	9	2.3%	<0.001 ***
	Unemployed	48	12.3%
	Student	230	58.8%
	Retired	6	1.5%
	Employed	98	25.1%
Mother’s current employment status	Other	54	13.8%	<0.001 ***
	Unemployed	193	49.4%
	Student	6	1.5%
	Retired	35	9.0%
	Employed	103	26.3%
Father’s current employment status	Other	58	14.8%	<0.001 ***
	Unemployed	27	6.9%
	Student	8	2.0%
	Retired	99	25.3%
	Employed	199	50.9%

***, highly significant at p < 0.001. N, the total number of participants; n, the number of participants within each category; %, the percentage of participants in a category to the total.

).

3.2. Questionnaire Reliability and Internal Consistency

The Cronbach’s alpha reliability coefficients for the scales of knowledge (five items), attitudes (five items), and practices (five items) were 0.78, 0.79, and 0.80, respectively, indicating high internal consistency within each scale.

The overall reliability for the combined 15 items was also high at 0.90. These results suggested that the scales were reliable tools for measuring the constructs of KAP, offering credibility to any findings derived from their use (

Table 5. Reliability analysis.

ID	Scales	Number of Items	Cronbach’s Alpha Reliability
1	Knowledge	5	0.78
2	Attitudes	5	0.79
3	Practices	5	0.80
Total		15	0.90

).

3.3. Normality

The Kolmogorov–Smirnov test revealed significant deviations from normality for the constructs of knowledge (D(391) = 0.091, p < 0.001), attitudes (D(391) = 0.112, p < 0.001), and practices (D(391) = 0.096, p < 0.001). Consistently, the Shapiro–Wilk test also indicated significant departures from normality for knowledge (W(391) = 0.960, p < 0.001), attitudes (W(391) = 0.948, p < 0.001), and practices (W(391) = 0.955, p = 0.001).

3.4. Descriptive Statistics of the Constructs

The respondents were asked to rate their agreement following a five-point Likert scale ranging from 1, strongly disagree, to 5, strongly agree.

The descriptive statistics of waste segregation KAP among 391 respondents showed that participants generally rated high on a five-point Likert scale. The mean ± SD scores indicated a positive orientation toward waste segregation, with knowledge (mean ± SD = 3.59 ± 0.78) above the midpoint, suggesting a moderate spread of responses among the sample who had poor knowledge (3.6%), fair knowledge (5.9%), good knowledge (31.5), very good knowledge (42.2%), and excellent knowledge (16.9%) (Figure 2).

Attitudes (mean ± SD = 3.73 ± 0.77) above the midpoint also suggested a moderate spread of responses among the sample, which had poor attitudes (2.8%), fair attitudes (4.1%), good attitudes (27.4%), very good attitudes (43.2%), and excellent attitudes (22.5%) (Figure 2).

Practices (mean ± SD = 3.62 ± 0.76) were above the midpoint. Included in the sample were those who had poor practices (2.3%), fair practices (5.6%), good practices (33.8%), very good practices (41.9%), and excellent practices (16.4%) (Figure 2) (

Table 6. Descriptive statistics for knowledge, attitudes, and practices in terms of mean, median, mode, quartiles, and standard deviation (SD).

Variables	Mean	SD	Median	Mode	Percentiles		Chi-Square
					25	75
Knowledge Q1	3.5	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Knowledge Q2	3.6	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Knowledge Q3	3.6	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Knowledge Q4	3.6	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Knowledge Q5	3.8	1.0	4.0	4.0	3.0	5.0	<0.001 ***
Knowledge (average)	3.6	0.8	3.6	3.0	3.0	4.0	<0.001 ***
Attitude Q1	4.0	1.0	4.0	4.0	3.0	5.0	<0.001 ***
Attitude Q2	3.6	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Attitude Q3	3.5	1.1	4.0	4.0	3.0	4.0	<0.001 ***
Attitude Q4	3.9	0.9	4.0	4.0	3.0	5.0	<0.001 ***
Attitude Q5	3.8	1.0	4.0	4.0	3.0	5.0	<0.001 ***
Attitudes (average)	3.7	0.8	3.8	4.0	3.2	4.2	<0.001 ***
Practice Q1	3.7	1.1	4.0	4.0	3.0	5.0	<0.001 ***
Practice Q2	3.9	1.0	4.0	4.0	3.0	5.0	<0.001 ***
Practice Q3	3.3	1.1	3.0	3.0	3.0	4.0	<0.001 ***
Practice Q4	3.7	1.0	4.0	4.0	3.0	4.0	<0.001 ***
Practice Q5	3.6	1.0	4.0	4.0	3.0	4.0	<0.001 ***
Practices (average)	3.6	0.8	3.6	4.0	3.0	4.0	<0.001 ***

***, significant at p < 0.001; SD = standard deviation, Knowledge Q1-5 = knowledge questions, knowledge (average) = mean of total knowledge marks, Attitude Q1-5 = attitude questions, Attitudes (average) = mean of the total attuited points, Practice Q1-5 = practices questions, Practices (average) = mean of total practice marks.

and

Table 7. Descriptive statistics for knowledge, attitudes, and practice in terms of mean, median, mode, quartiles, and standard deviation (SD).

Variables	Strongly Disagree		Disagree		Neutral		Agree		Strongly Agree		Chi-Square
	n	%	n	%	n	%	n	%	n	%
Knowledge Q1	31	7.9	32	8.2	121	30.9	134	34.3	73	18.7	<0.001 ***
Knowledge Q2	25	6.4	38	9.7	87	22.3	159	40.7	82	21.0	<0.001 ***
Knowledge Q3	22	5.6	36	9.2	107	27.4	155	39.6	71	18.2	<0.001 ***
Knowledge Q4	14	3.6	48	12.3	113	28.9	136	34.8	80	20.5	<0.001 ***
Knowledge Q5	12	3.1	26	6.6	89	22.8	165	42.2	99	25.3	<0.001 ***
Attitude Q1	17	4.3	15	3.8	72	18.4	153	39.1	134	34.3	<0.001 ***
Attitude Q2	18	4.6	43	11.0	106	27.1	139	35.5	85	21.7	<0.001 ***
Attitude Q3	26	6.6	48	12.3	105	26.9	148	37.9	64	16.4	<0.001 ***
Attitude Q4	10	2.6	13	3.3	81	20.7	186	47.6	101	25.8	<0.001 ***
Attitude Q5	17	4.3	23	5.9	90	23.0	152	38.9	109	27.9	<0.001 ***
Practice Q1	16	4.1	32	8.2	108	27.6	137	35.0	98	25.1	<0.001 ***
Practice Q2	12	3.1	19	4.9	87	22.3	159	40.7	114	29.2	<0.001 ***
Practice Q3	23	5.9	63	16.1	124	31.7	123	31.5	58	14.8	<0.001 ***
Practice Q4	12	3.1	24	6.1	122	31.2	151	38.6	82	21.0	<0.001 ***
Practice Q5	17	4.3	42	10.7	102	26.1	163	41.7	67	17.1	<0.001 ***

***, significant at p < 0.001, knowledge Q = knowledge questions, attitude Q = attitude questions, practice Q = practices questions, n = number of the participants in each category, % = percentage of participants.

).

3.5. Correlation Tests of Waste Segregation KAP and Sociodemographic Variables

The correlations of waste segregation KAP with sociodemographic variables revealed a positive relationship between gender and knowledge (p < 0.001), indicating a solid association where higher knowledge levels were linked with gender for waste segregation (

Table 8. Spearman’s correlations of waste segregation knowledge, attitudes, and practices with sociodemographic variables.

		Gender	Age	The Highest Level	Mother’s Education Level	Father’s Education Level	Employment	Mother’s Employment	Father’s Employment	Knowledge	Attitude
Knowledge	r	0.180	0.046	0.087	−0.035	−0.071	−0.012	−0.029	0.034	--	0.645
	p	0.002 **	0.364	0.086	0.487	0.163	0.812	0.571	0.503	--	<0.001 ***
Attitudes	r	0.118	0.056	0.024	−0.091	−0.082	−0.001	−0.085	−0.029	0.645	---
	p	0.019 *	0.267	0.634	0.071	0.106	0.986	0.095	0.574	<0.001 ***	---
Practices	r	0.097	0.086	0.065	−0.106	−0.027	0.011	−0.042	−0.04	0.663	0.708
	p	0.055	0.088	0.201	0.036 *	0.601	0.824	0.412	0.426	<0.001 ***	<0.001 ***

*, **, *** Correlation was significant at p < 0.05, p < 0.01, p < 0.001 (2-tailed); r, Spearman’s correlation coefficient; p, two tailed significance test.

). The correlation between attitudes and the mother’s current employment status was negatively correlated (r = −0.101), indicating that the mother’s current employment status does not affect more favorable attitudes toward waste segregation (Table 8).

Knowledge and attitudes were positively correlated (r = 0.645, p < 0.001), indicating a solid association where higher knowledge levels were linked with more positive attitudes toward waste segregation. Similarly, knowledge and practices were significantly correlated (r = 0.663, p < 0.001), suggesting that increased knowledge about waste segregation was associated with more waste management practices (Table 8).

The correlation between attitudes and practices was also strong (r = 0.708, p < 0.001), indicating that more favorable attitudes toward waste segregation were associated with more waste management practices (Table 8). These significant correlations at the 0.01 level (2-tailed) suggested a robust interrelationship among KAP related to waste segregation, highlighting the interconnectedness of these constructs in the context of waste segregation (Table 8).

Table 9. Logistic regression for the effect of gender, age, educational level (participants, mothers, fathers), and employment status (participants, fathers, mothers) on average knowledge, attitude, and practice scores.

Effect	Knowledge		Attitude		Practice
	Chi-Square	p Value	Chi-Square	p Value	Chi-Square	p Value
Gender	4605.8	<0.001 ***	18.0	0.525 ns	208.1	<0.001 ***
Age	21,766.5	<0.001 ***	61.5	0.886 ns	96.3	0.058 ns
The highest level/degree of education	31.1	0.039 *	14.9	0.731 ns	160,868.5	<0.001 ***
Mother’s education level	17.6	0.552 ns	21.4	0.315 ns	149.0	<0.001 ***
Father’s education level	467.6	<0.001 ***	26.2	0.125 ns	240.1	<0.001 ***
Current employment status	12.9	0.844 ns	21.5	0.310 ns	1571.3	<0.001 ***
Mother’s current employment status	193.4	<0.001 ***	24.6	0.175 ns	398.9	<0.001 ***
Father’s current employment status	28.9	0.068 ns	87.9	0.166 ns	456.1	<0.001 ***

*, significant at p < 0.05; ***, highly significant at p < 0.001; ns, nonsignificant at p > 0.05.

represents the results of logistic regression for the effect of gender, age, educational level (participants, mothers, fathers), and employment status (participants, fathers, mothers) on average KAP scores. According to logistic regression, gender has a highly significant effect (p < 0.001 ***) on knowledge and practice and a nonsignificant effect on attitudes (Table 9).

Age had a highly significant effect (p < 0.001 ***) on knowledge and a nonsignificant effect on attitudes and practice. The highest educational level of participants had a significant effect on both knowledge (p = 0.039 *) and practice (p < 0.001 ***) and nonsignificant effect on attitudes (Table 9). Generally, gender, the highest educational level, the father’s educational level, and the mother’s employment status significantly affected practice. However, all sociodemographic parameters had a nonsignificant impact on attitude (Table 9).

In addition, gender, the highest educational level, the mother’s and father’s educational level, current employment status, the mother’s employment status, and the father’s employment status significantly affected practices (Table 9).

3.6. Regression of Waste Segregation Practice on Attitudes and Knowledge

The regression analysis demonstrated a significant and impactful relationship between KAP and waste segregation. The results highlighted the importance of knowledge and attitudes in influencing practices, with attitudes having a slightly more pronounced effect.

The model summary (

Table 10. Waste segregation practice model summary ^a.

Model	R	R2	Adj. R2	SE	Change Statistics					Durbin-Watson
					R2 Change	F-Change	df1	df2	p Value
1	0.783 b	0.613	0.611	0.47931	0.613	307.04	2	388	0.001 ***	2.019

^a Independent variables (predictors): constant, attitudes, knowledge. ^b Dependent variable: practices. ***, highly significant at p < 0.001. R, correlation coefficient (0, no correlation; 0.1–0.3 weak correlation, 0.31–0.60 moderate correlation; >0.60 strong correlation); R², the coefficient of determination; Adj.R², is the determination coefficient adjusted for the number of independent variables; SE, standard error; R² change, the change in R²; F-change, the change in F-ratio; df1, degree of freedom for variables (=number of independent variables); df2, degree of freedom in number of participants (=denominator degrees of freedom); Durbin-Watson statistic is a test for autocorrelation.

) shows that the predictors, knowledge, and attitudes strongly correlate with practices (R = 0.783), indicating a relationship. The R square value of 0.613 suggested that 61.3% of the variance in practices was explained by the model, a substantial proportion. The adjusted R square, at 0.611, adjusts this value slightly for the number of predictors but still indicates a significant explanatory power. The Durbin–Watson statistic of 2.019 pointed to a moderate level of autocorrelation in the residuals, which was within the acceptable limits and did not detract from the model’s validity.

The ANOVA results (

Table 11. Waste segregation practices ANOVA ^a.

Model		SS	df	MS	F-Ratio	p Value
1	Regression	141.08	2	70.54	307.04	0.001 b
	Residual	89.14	388	0.23
	Total	230.21	390

^a Dependent variable (practices). ANOVA, analysis of variance; ^b Independent variable (predictors: constant, attitudes, and knowledge). SS, sum of square; df, degree of freedom; MS, mean squares.

) further confirmed the model’s effectiveness. The regression model was statistically significant (F(2, 388) = 307.038, p < 0.001), demonstrating a strong relationship between the independent variables (knowledge and attitudes) and the dependent variable (practices). The division of total variance into regression (141.077) and residual (89.139) parts underlined the model’s capacity to account for significant practice variability.

The coefficients table (

Table 12. Waste segregation practice coefficients ^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	p Value	Collinearity Statistics
		B	SE	Beta			Tolerance	VIF
1	(Constant)	0.524	0.128		4.104	0.001 ***
	Knowledge	0.330	0.041	0.339	8.013	0.001 ***	0.556	1.797
	Attitudes	0.513	0.042	0.515	12.156	0.001 ***	0.556	1.797

^a Dependent variable (practices). ***, highly significant at p < 0.001. SE, standard error; VIF, variance inflation factor; t, t-statistics.

) provides detailed insights into the individual predictors. Both knowledge and attitudes are significant predictors of practices, with knowledge having a positive effect (B = 0.330, t = 8.013, p < 0.001) and a moderate influence (standardized Beta = 0.339) (Table 12). Attitudes showed a more substantial positive effect on practices (B = 0.513, t = 12.156, p < 0.001), with a higher Beta value of 0.515, indicating a more substantial impact on practices (Table 12). The collinearity statistics for both predictors are within acceptable ranges, suggesting that multicollinearity was not a concern in this model.

Figure 3 represents a red–blue heatmap presenting the interrelationship between study variables. Red indicates a negative correlation, blue indicates a positive correlation, and white indicates no correlation. The two-tailed significance test revealed the boxed colors for significant correlation. According to the heatmap, a significant positive correlation exists between participants’ educational level and total knowledge scores (Figure 3).

There was a significant positive correlation between KAP. In addition, there was a significant positive correlation between knowledge scores, attitudes, and practices (Figure 3).

Figure 4 summarizes the effect of the main study variables on participants’ responses. The two axes, PCA-1, and PCA-2, represented 25.5% and 24.33% of total study variables. The shaded area represented 95% confidence. The PCA ordination revealed a marked effect on educational level, father’s educational level, and mother’s educational level, which appear in the upper right of the PCA (Figure 4).

4. Discussion

In Northwest Ethiopia, Deress et al. [44] demonstrated a correlation between knowledge with attitudes, knowledge with practices, and attitudes with practices [44]. Our findings in the current study showed a significant moderate correlation between attitude and practices (r = 0.741, p < 0.001), indicating that more favorable attitudes toward waste segregation were associated with more waste management practices. Moreover, there was a positive correlation between knowledge and attitude (r = 0.666, p < 0.001) and a positive correlation between knowledge and practices (r = 0.682, p < 0.001) in the present study.

A significant proportion of participants in the study conducted by Badrum and bin Mapa [45] in Penampang Proper Village, Sabah, Malaysia, demonstrated a strong knowledge, a positive attitude, and effective practice of solid waste management practices KAP. While the level of knowledge and attitude was typically high, the study revealed no statistically significant relationship between them and the actual practices [45]. The practice level of waste management was shown to be substantially connected with two sociodemographic variables: year of birth and levels of education. This suggests that individuals who are younger and more educated are likely to have a higher predisposition to engage in better waste management practices [45].

Furthermore, Badrum and bin Mapa’s [45] study revealed a direct relationship between knowledge and attitude, suggesting that individuals with greater levels of education are more inclined to hold good attitudes about trash management [45]. Similarly to our findings, there was a positive correlation between knowledge and practices. Moreover, the current study revealed a positive correlation (r = 0.159, p < 0.001) between general waste segregation KAP and sociodemographic variables. This indicates a strong association, suggesting that higher levels of knowledge are linked to gender concerning waste segregation. In addition, there was a significant correlation (r = 0.110) between attitude and gender, indicating that gender was associated with increased knowledge about waste segregation.

In Vietnam, Giao and Thien [46] analyzed waste composition and community KAP to evaluate the elements that impact KAP in domestic solid waste management. The goal was to create a comprehensive plan for effective domestic waste management. The Chi-square test demonstrated a statistically significant correlation between the KAP level of the participants and demographic characteristics (p < 0.05). The key characteristics that exerted a substantial influence were the amount of education and age [46]. The current study reported a significant positive association (r = 0.159, p < 0.001) between KAP related to general waste segregation and sociodemographic characteristics. This suggests a robust correlation, indicating a connection between higher levels of knowledge and gender regarding waste segregation.

In addition, a separate study conducted in Nakuru town, Kenya, by Kamweru [47] examined various types of solid waste generated by families, assessed residents’ attitudes toward existing solid waste management systems, and ascertained the levels of KAP among households and authorities. Unfavorable practices were witnessed, such as the random disposal of waste in exposed locations and drainage channels, as well as the burning and burial of solid waste in dug-out pits. The cross-tabulation analysis revealed a substantial impact of respondents’ age on attitudes toward solid waste management operations (p < 0.001). Kamweru’s [47] study also found a strong link (p < 0.001) between the educational attainment of residents and their behavior toward solid waste management [47]. The analysis in the current study revealed a noteworthy correlation (r = 0.110) between attitude and gender, suggesting that gender was linked to a higher level of understanding regarding segregation. Moreover, there was a negative connection (r = −0.101, p < 0.001) between attitudes and the mother’s present employment situation.

5. Conclusions

This study highlights the critical relationship between KAP and waste segregation among UAE residents. The data demonstrated substantial correlations that indicated present behaviors and emphasized possibilities for policy interventions and educational programs designed to improve waste management practices.

In the current study, the KAP level of the consequences of waste segregation in the UAE population was as follows. knowledge (mean ± SD = 3.59 ± 0.78)—among the sample, ratings were as follows: those who had poor knowledge (3.6%), fair knowledge (5.9%), good knowledge (31.5), very good knowledge (42.2%), and excellent knowledge (16.9%); attitudes (mean ± SD = 3.73 ± 0.77)—among the sample, ratings were as follows: those who had poor attitudes (2.8%), fair attitudes (4.1%), good attitudes (27.4%), very good attitudes (43.2%), and excellent attitudes (22.5%); and practices (mean ± SD = 3.62 ± 0.76)—among the sample, ratings were as follows: those who had poor practices (2.3%), fair practices (5.6%), good practices (33.8%), very good practices (41.9%), and excellent practices (16.4%).

Furthermore, the present study demonstrated a positive correlation between knowledge and attitude (r = 0.666, p < 0.001) and a positive correlation between knowledge and practices (r = 0.682, p < 0.001), and this answered our research question 1. Also, there was a positive correlation (r = 0.159, p < 0.001) between general waste segregation KAP and sociodemographic variables, in which there was a significant correlation (r = 0.110) between attitude and gender and a negative correlation (r = −0.101, p < 0.001) between attitudes and the current employment status of the mother. This answered research question 2 and proved H₁ was correct, which is that the sociodemographic impact of KAP and regret H0 do not affect each other.

Long-term monitoring and evaluation are crucial for ensuring the continued success of waste segregation initiatives. Implementing robust systems for ongoing monitoring and evaluation of these programs will enable stakeholders to monitor important metrics, such as waste diversion rates, public awareness, and changes in attitudes toward waste management. These metrics are crucial for evaluating the efficiency of implemented strategies and identifying areas that need additional improvement or support.

Future endeavors should prioritize broadening the research scope to encompass a wide range of demographic segments, thus facilitating the development of more customized and efficient waste management strategies. Systematic evaluation and education intervention will be crucial in achieving the UAE’s sustainability goals and promoting responsible waste management by facilitating continuous adaptation and improvement.