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Abstract

Public awareness and understanding of air quality in Central Asia remain considerably low. The present study assesses the perception, attitude and environmental knowledge of local air quality among adult urban residents (n = 870) in a city with high air pollution among cities of Central Asia: Astana, Kazakhstan. Structural equation modelling (SEM) was employed to investigate the causal relationship between perceived air quality, environmental literacy and willingness to pay for environmental protection. Over half of the Kazakhstani population has higher education, yet environmental literacy remains low compared to countries with fewer university graduates. Participants’ age, education and health status significantly affected (p < .001) their environmental knowledge and awareness. The SEM indicates knowledge as a major determinant in improving public awareness and perception of air pollution. The present study provides valuable insights for researchers and governmental institutions to promote a better understanding of air quality within a rapidly growing urban environment.

Keywords

Ambient air pollutants community health environmental health literacy inhalation exposure multivariate analysis particulate matter

Introduction

A large body of theoretical and empirical environmental research demonstrates that exposure to ambient air pollutants such as particulate matter (PM) and gaseous pollutants (e.g., CO, SO_x, NO_x and O₃) is directly linked to adverse health outcomes (such as asthma, bronchitis, cardiovascular conditions) (Aztatzi-Aguilar et al., 2018; Farina et al., 2011; Kelly & Fussell, 2012; Xue et al., 2019). As a developing country, Kazakhstan is currently experiencing rapid economic development, increased exploitation of resources, population growth and intensive urbanization. As a result, air pollution is a major public health concern. Respiratory diseases account for 43.5% of the population’s morbidity (Abakhanov, 2020). Moreover, in Kazakhstan, exposure to PM is linked to 2,800 premature deaths each year (Kerimray et al., 2018).

Astana is the capital city of Kazakhstan and is characterized by extremely harsh climate conditions with long and cold winters, leading to a heating period via central heating plants that lasts from October to April. A high concentration of air pollutants, such as PM_2.5, is particularly present during the heating period. For example, the concentration of PM_2.5 during wintertime ranges between 100 and 200 µg/m³ on several days (Assanov et al., 2021). Recent environmental legislation in Kazakhstan, effective since December 2019 with 75 new amendments, has not improved environmental quality or established permissible pollutant concentrations (Abakhanov, 2020). Moreover, studies in Kazakhstan often focus on ambient air pollution levels, neglecting social aspects, while public knowledge about air pollution remains low.

The sociological aspect of air pollution research (e.g., attitude, behaviour intentions, anxiety related to air quality) is a component that can increase public involvement in mitigating risks associated with air pollution and may determine the success of potential public interventions. Most of the sociological studies in this domain investigate the association between demographic characteristics and perception, attitude and knowledge of air pollution (Odonkor et al., 2020; Qian et al., 2016; Wang et al., 2015). Other studies focus on the perceived air pollution risks in urban environments (e.g., levels and sources of air pollution and related health effects) (Maione et al., 2021; Saksena, 2012), individual intervention strategies to reduce exposure to ambient air pollutants (Janjua et al., 2019; Laumbach et al., 2015), information channels about air pollution-related topics and pro-environmental behaviour and challenges in behaviour change (Carducci et al., 2021; Ramírez et al., 2019).

Environmental attitudes, crucial in environmental psychology, reflect individual predispositions towards the natural environment with varying degrees of favourability or unfavourability (Milfont, 2007). Measuring environmental attitudes is challenging due to their multidimensional nature (Milfont & Duckit, 2010). Traditionally, environmental attitudes consist of cognitive (e.g., thoughts and ideas regarding the subject of the attitude), affective (e.g., emotional response) and behavioural elements (Steg et al., 2014). The theory of planned behaviour explains the complex nature of individual behaviour, suggesting that it is dictated by behavioural intentions that are also influenced by internal factors such as personal attitudes, perceptions and subjective norms in a particular situation. Essentially, a strong intention to engage in a certain behaviour reinforces it. Individual behaviour also depends on the degree of perceived behaviour control (Ajzen, 1991). The theory of planned behaviour was adopted in the context of environmental behavioural intentions.

Environmental behaviour is greatly influenced by perceived risks that shape individual behaviour intentions; thus, the individual motivation for behaviour change is highly correlated with a negative outcome (e.g., adverse health effects) and with the perceived probability of that outcome (Keller et al., 2012; Li & Hu, 2018; Saari et al., 2021). Moreover, research also suggests that environmental consciousness is defined by the perceived knowledge of what an individual can or cannot do (Hungerford & Volk, 1990; Keller et al., 2012; Li & Hu, 2018; Saari et al., 2021). This is especially important in the context of willingness to pay (WTP) for environmental protection, which is largely influenced by knowledge of the related topic (Vassanadumrongdee & Matsuoka, 2005). Moreover, the awareness of the general public about air pollution can be built through knowledge-increasing tools (i.e., media and online resources that publish information on air pollution topics) (Li & Hu, 2018; Rajper et al., 2018; Ramírez et al., 2019).

Based on the literature reviewed, the following hypotheses were set for the present study:

Environmental attitudes affect the perception of national air pollution and the economy. Environmental attitudes being a key component of individuals’ perceptions of the economic impact of air pollution can guide their support for environmental regulations and policy implementation, while also providing a basis for policymakers to effectively address environmental issues considering economic factors.

Institutional knowledge affects the perception of national air pollution and the economy. Institutional knowledge, which refers to a collective understanding of environmental topics within organizations or institutions can shape environmental perception and decision-making regarding air pollution and its economic implications.

Knowledge of local air quality affects the perception of national air pollution and the economy. Awareness of local air quality can shape public perception of national pollution levels and their economic, social and individual impacts which are crucial for effective environmental management.

WTP affects environmental attitudes. A high WTP typically indicates a strong environmental attitude reflected in concern for the environment and support for environmental conservation.

The present study aims: (i) to assess the level of knowledge of air pollution and its health effects among adult urban residents in the capital city of an emerging economy (Astana, Kazakhstan; an area of very high air pollution levels according to the Air Pollution Index [API]); (ii) to evaluate the public perception of air pollution in the city; (iii) to assess the attitude of the studied population towards environmental protection; and (iv) to estimate the relationship between knowledge, attitude and perception using the structural equation modelling (SEM) approach. The present study evaluates public understanding of air pollution in a heavily polluted area with a developing economy, with implications for health interventions and policy. It also aims to assess pollution-related health risks, address knowledge gaps and promote sustainable practices.

Background

Structural Equation Modelling

SEM enables multivariate analysis of variable relationships, rising in popularity due to its comprehensive evaluation capabilities (Hair et al., 2018, 2017; Lee et al., 2011). It integrates exploratory factor and structural path analyses, explaining more variance than multiple regression.

PLS-SEM is preferred for its robustness with non-normal data, typical in social sciences (Hair et al., 2017). Furthermore, PLS-SEM allows for predictive exploratory analysis, rather than confirmation and explanation of certain theories, as in covariance-based [CB] SEM. In the present study, we do not aim to confirm theories, rather, we would like to explore which of the identified variables could be the key drivers of the latent constructs. Reflective models, such as in the present study, can be effectively analysed with both PLS and CB. Given ordinal data in collected survey responses, PLS-SEM is more effective and thus utilized in the present study.

Environmental Attitude

Attitudes are defined as positive or negative relationships or beliefs towards something (Kollmuss & Agyeman, 2002). Environmental attitudes are shaped by individual values, which can influence perceptions, feelings and behaviours regarding environmental protection. In addition, education is another important factor that can impact environmental attitudes (Arslan, 2012; Erhabor et al., 2016). Thus, changes in the latent construct of ‘environmental attitude’ are expected to be mirrored across beliefs, personal values and education.

Perception of National Air Pollution and Economy

An informed understanding of environmental issues is often associated with opinions on economic development versus environmental protection (Hanley et al., 2009; Voorhees et al., 2001), shaping attitudes towards balancing economic growth and environmental sustainability, individual efficacy in addressing pollution and the role of science, technology and governmental involvement (Hanley et al., 2009; Voorhees et al., 2001).

Willingness to Pay

Individuals’ evaluation of non-market goods, such as air quality, can be impacted by their willingness to accept personal costs. Thus, ‘willingness to pay’, as a latent construct, is reflectively influenced by individual values and preparedness for sacrifices for environmental protection (Akhtar et al., 2017; Carlsson et al., 2000).

Institutional Knowledge

In a reflective model, ‘institutional knowledge’ shapes individual understanding of environmental issues and perceptions of policy and economic trade-offs (Gupta, 2018). Effective knowledge dissemination is a key strategy for environmental awareness further underscores the importance of strategic knowledge in enhancing public consciousness of air pollution (Loopmans et al., 2022).

Knowledge of Local Air Quality

Understanding environmental issues, including air quality, influences individual perceptions and beliefs. Knowledge about science and local industrial development can reflect attitudes towards air quality (Bickerstaff & Walker, 2001; Kelly & Fussell, 2015).

Methodology

Study Area

The present work is a cross-sectional study of the urban adult population of Astana, Kazakhstan. The city’s population was estimated at 1,239,744 at the start of 2022, with 594,742 male and 645,002 female residents, respectively. The average age of Astana residents was 30.1 years in 2021. At the beginning of 2022, 795,969 adults (≥20 years) were registered in Astana (National Bureau of Statistics, 2022).

Astana’s population has grown rapidly (11.2% increase in 2021 compared to 2020, National Bureau of Statistics, 2022), leading to increased transportation demand (26 vehicles per 100 people) (Assanov et al., 2021). Heavy traffic, coal-powered plants (CHPPs) and residential heating contribute to air pollution exposure, particularly during winter (Assanov et al., 2021; Kerimray et al., 2018).

Instrumentation

The framework of the survey instrument was adapted from Chin et al. (2019). The 32-item questionnaire was created via an online research software, Qualtrics (Qualtrics LLC, UT, US), in the form of a self-administered questionnaire. The snowballing sampling technique has been used. The online questionnaire was designed in three languages: English, Kazakh and Russian, to guarantee that respondents could comfortably respond to questions in their preferred language.

The survey questionnaire was divided into three sections: The first part contained seven questions on the socio-demographic parameters of the studied population (age, gender, education, employment status, work environment, average household income and chronic health conditions). The second part contained three questions to assess the awareness of the studied population about air pollution. This section had three multiple-choice questions to understand the perception of the general public on air quality in the region, knowledge about potential sources of air pollution and the sources of information regarding air pollution-related topics. It also contained six true/false questions further evaluating participants’ knowledge of air pollution monitoring systems, sources of gaseous pollutants, air pollution-related indicators (e.g., API), and air pollution-related health effects. The third part evaluated the participants’ attitudes towards environmental protection and contained 16 statements on a 5-point Likert scale, covering the attitude of the studied population towards economic costs and governmental pollution management prices, as well as WTP for environmental protection.

Data Collection

The survey responses were collected during May and June of 2022. Two rounds of pre-testing with 20 respondents were conducted to ascertain the correct and rational interpretation of the survey questions. The link to the anonymous survey was distributed through social media platforms and the university’s mail services. A total of 870 responses were collected. Incomplete, ‘straight-line’ and inconsistent survey responses were excluded from further analysis, leading to a final total of 782 responses that were analysed. The present research received prior approval from the Institutional Research Ethics Committee of Nazarbayev University (NU IREC).

Statistical Analysis

With regard to the assessment of knowledge of potential sources of air pollution in Section 2a of the questionnaire, each correct answer was given 1 point, leading to a total of 8 maximum points. In Section 2b, 1 point was given for a correct answer, −0.2 for an incorrect response, and 0 points when answered ‘I don’t know’ (a total of 6 points). In Section 3 of the questionnaire, a higher score on a 5-point Likert scale indicated more positive attitudes in statements 17, 19, 21, 25, 27, 28, 29, 31 and 32; whereas statements 20, 22, 23, 26 and 30 were scored in reverse.

Four statements (#17, 23, 26 and 30) represent the affective component of the attitude scale, whereas five statements (#18, 25, 27, 28 and 32) denote the cognitive element. The conative component of the attitude scale is reflected in the five statements related to WTP for environmental protection (statements #19, 21, 24, 29 and 31).

The statistical analyses, including descriptive analysis, t-tests and chi-square association tests have been conducted via Stata 14.2 by StataCorp. 2015 (TX, US) to assess the relationships between knowledge about air pollution, concerns about air quality, attitudes towards environmental protection and demographic characteristics.

SEM and Model Validity

The SEM tool was used to evaluate the proposed model’s reliability and validity and to test the hypotheses set. Table 1 and Figure 1 represent the SEM’s latent, observable variables, related questions and the model itself. The model is reflective, as the observed variables manifest the behaviour of the latent construct. First, the partial least squares technique was used to identify the path loadings. Each latent variable was described through a minimum of two observable variables. Then, to check the hypothesis, bootstrapping was used to derive heterotrait–heteromethod correlation statistics, thus, calculating the standard error, which helps to identify the bootstrap confidence interval (95% limit). The model’s validity was then checked through statistical values including outer loadings, Cronbach’s alpha, average variance extracted, Dillon–Goldstein’s rho and composite reliability. The acceptance criteria were the following: outer loadings > 0.7, Cronbach’s alpha > 0.7, Average variance extracted > 0.5, Dillon–Goldstein’s rho (rho_A) > 0.7 and composite reliability > 0.7.

Table 1.

Latent Observable Variables and Their Corresponding Questions.

Latent Variable	Observable Variable	Question
Environmental attitude	EA1	Educating the younger generation about the knowledge of environmental protection is important.
	EA2	Taking care of the environment is something I really care about.
	EA3	Air pollution caused by cars is extremely dangerous to health.
	EA4	To protect the environment, Kazakhstan needs economic growth.
	EA5	I do not mind paying more money to use better-quality gasoline, which leads to less pollution.
	EA6	Protecting the environment should be given priority, even if it causes slower economic growth.
Perception of national air pollution and economy	P1	Air pollution is a fair price to pay for economic development.
	P2	The economic growth of Kazakhstan is currently more important than environmental protection.
	P3	There is no point in doing what I can for the environment unless everyone does the same.
	P4	Kazakhstanis worry too much about industrial development polluting the atmosphere and degrading human health.
	P5	Nothing can be done by me or my family/friends to improve the current atmospheric situation.
	P6	The air quality in Kazakhstan is getting better because of modern science and technology.
	P7	Kazakhstan’s government has to reduce atmospheric pollution, but it should not cost me any money.
WTP	WP1	I do not mind an increase in taxes if the extra money is used to prevent further atmospheric pollution.
	WP2	I am willing to accept cuts in my standards of living to protect the environment.
	WP3	I would contribute part of my income if I were certain that the money would be used to prevent atmospheric pollution.
Institutional knowledge	KoI1	Sulfur dioxide (SO₂), which is a serious air pollutant, mainly comes from coal-fired power plants and residential heating.
Institutional knowledge	KoI2	Air pollution is a fair price to pay for economic development.
Knowledge of local air quality	KoLAQ1	The air quality in Kazakhstan is getting better because of modern science and technology.
Knowledge of local air quality	KoLAQ2	Kazakhstanis worry too much about industrial development polluting the atmosphere and degrading human health.

Figure 1.

Proposed SEM Model.

Results

Socio-demographic Characteristics of Studied Population

The general demographic characteristics of the participants (Table 2) show that out of the total 782 respondents, the most prevalent age group of the participants was 25–34 years old (29.2% [n = 228]). Half of the respondents had at least a bachelor’s degree (49.9% [n = 390]). More than a third of the study population (41.7% [n = 326]) was identified as unemployed, while 30.3% [n = 237] were self-employed. The majority (54.0% [n = 422]) of the participants described the environment they spend most of their time in as an environment where air quality is not a concern, whereas 10.1% [n = 79] and 11.0% [n = 86]) were engaged in industrial and urban sectors, respectively. Only 37.2% [n = 291] of the participants reached the average wage of the Kazakhstani population (263,905 KZT in 2022) (Bureau of National Statistics, 2022).

Table 2.

General Demographic Characteristics of the Respondents (N = 782).

Demographic Characteristics	N	%
Age category
18–24	59	7.60
25–34	228	29.2
35–44	184	23.6
45–54	147	18.8
55–64	76	9.70
65+	87	11.1
Gender
Male	365	46.7
Female	415	53.1
Undefined	2	0.3
Education
Secondary school degree	8	1.0
High school degree	72	9.2
Specialized secondary education	233	29.8
Bachelor’s degree	390	49.9
Master’s degree or above	79	10.1
Employment status
Full-time employment	106	13.6
Part-time employment	69	8.8
Self-employed	237	30.3
Unemployed	326	41.7
Student	40	5.1
Retired	4	0.5
Environment
Not a concern	422	54.0
Industrial environment/concern	79	10.1
Urban environment/concern	86	11.0
Another environment/concern	22	2.8
Not working	52	6.7
Home environment	121	15.5
Average monthly household income
<100,000 KZT	282	36.1
100,000–250,000 KZT	291	37.2
250,001–500,000 KZT	98	12.5
500,001–1,000,000 KZT	51	6.5
>1,000,000 KZT	60	7.7
Health status
High blood pressure
Yes	190	24.3
No	591	75.7
Cardiovascular conditions
Yes	21	2.7
No	760	97.3
Diabetes
Yes	8	1.00
No	773	99.0
Asthma
Yes	11	1.40
No	770	98.6
COPD
Yes	12	1.50
No	769	98.5

SEM Validity Check

The model underwent validity checks using partial least squares analysis, with hypotheses confirmed/rejected via bootstrapping. Convergent validity check results (Table 3) showed most values exceeding the 0.7 limit, indicating strong representation except for KoAPI1. Lower values (P5, P3 and P7) remained within acceptable limits (0.4‒0.7). Reliability and validity checks (Table 4) revealed institutional knowledge as the least reliable construct due to limited questions (Hair et al., 2018).

Table 3.

Convergent Validity.

Variable	Outer Loading	Variable	Outer Loading
P1	0.447	EA4	0.726
P2	0.462	P4	0.622
WP1	0.872	P5	0.675
WP2	0.772	EA5	0.824
P3	0.468	EA6	0.752
EA1	0.769	P6	0.544
WP3	0.683	P7	0.707
KoI1	0.134	KoI2	0.996
EA2	0.791	KoLAQ2	0.765
AM3	0.836	KoLAQ1	0.807

Table 4.

Construct Reliability and Validity.

	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)
Environmental attitude	0.875	0.878	0.905	0.615
Institutional knowledge	0.090	0.534	0.563	0.505
Knowledge of local air quality	0.381	0.383	0.763	0.618
Perception of national air pollution and economy	0.670	0.693	0.765	0.324
WTP	0.684	0.750	0.822	0.608

Table 5 demonstrates the values for discriminant validity which indicates the differences between the constructs. All the values are distinct and discriminant. Table 6 represents the hypothesis test check using bootstrapping, showing that all four hypotheses are supported.

Table 5.

Discriminant Validity of Constructs.

	Environmental Attitude	Institutional Knowledge	Knowledge of National Air Pollution	Perception of National Air Pollution and Economy	WTP
Environmental attitude	0.78
Institutional knowledge	–0.02	0.71
Knowledge of national air pollution	0.27	0.35	0.79
Perception of national air pollution and economy	0.22	0.71	0.82	0.57
WTP	0.53	0.03	0.26	0.23	0.78

Table 6.

Hypothesis Test Results.

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	t statistics (\|O/STDEV\|)	P
Environmental attitude → Perception of national air pollution and economy	0.06	0.06	0.02	3.85	<.001
Institutional knowledge → Perception of national air pollution and economy	0.49	0.49	0.03	19.76	<.001
Knowledge of local air quality → Perception of national air pollution and economy	0.63	0.63	0.03	25.46	<.001
WTP → Environmental attitude	0.53	0.54	0.03	18.92	<.001

Table 7 shows that the effect of knowledge of local air quality on the perception of national air pollution and the economy is the strongest (i.e., with the largest path value: 0.626). The effects of institutional knowledge on the perception of national air pollution and economy and WTP on environmental attitude are also strong (0.492 and 0.533, respectively). Interestingly, the effect of environmental attitude on the perception of national air pollution and the economy is minimal.

Table 7.

Path Coefficient Values.

Path	Coefficient
Environmental attitude → perception of national air pollution and economy	0.063
Institutional knowledge → Perception of national air pollution and economy	0.492
Knowledge of local air quality → perception of national air pollution and economy	0.626
WTP → Environmental attitude	0.533

Relationship Between Air Quality Perception and Socio-demographics

The public perception of air quality in Astana was affected by participants’ age, gender, education, employment status, the environment in which the respondents spent most of their time, average monthly household income (p < .001) and health status (HBP [p < .001] and COPD [p = .005]) (Table S1, Supplementary Material).

Almost half of the respondents (42.1% [n = 329]) classified the air quality as moderately polluted and causing no harm. Among the respondents in the 35–44 age category, half (52.7% [n = 97]) considered the air quality in Astana moderately polluted, causing no detrimental effect to the general population. However, one-third of the respondents in the 45–54 and 55–64 age groups (34.3% [n = 50], and 30.3% [n = 23], respectively) classified the air quality as highly polluted.

Respondents with graduate degrees and full-time employment were more likely to consider the air quality level as moderately dangerous to human health (55.7% [n = 44]; 49.5% [n = 52], respectively). The majority (69.6% [n = 55]) of participants working in an industrial environment noted a high level of air pollution.

Respondents’ Knowledge of Air Pollution-related Topics

Table 8 summarizes the relationship between participants’ knowledge of air pollution-related topics (maximum score: 14.0) and demographics. Most participants consider industrial emissions, vehicle exhaust and coal burning as the major sources of air pollution (Figure 2). Furthermore, almost all respondents (99.9%) use online resources for air pollution-related information (Figure 3). The knowledge score was considerably low and varied significantly (p < .001) by age group, education, employment status, environment where respondents spend most of their time and average monthly household income. A higher level of knowledge was noted among participants in the 18–24 age group (mean = 6.75 ± 3.14 [p < .001]). Overall, participants with a graduate degree demonstrated a higher level of knowledge of air pollution-related topics (mean = 6.44 ± 2.84 [p < .001]).

Table 8.

Level of Knowledge of Air Pollution Among Respondents.

Demographic Characteristics	Level of Knowledge of Air Pollution
Demographic Characteristics	Mean
Age category
18–24	6.75 ± 3.14
25–34	4.32 ± 2.93	df = 5
35–44	3.84 ± 2.80	p < .001
45–54	4.31 ± 2.73
55–64	4.79 ± 2.87
65 +	4.67 ± 2.20
Gender
Male	4.70 ± 2.78	df = 2
Female	4.27 ± 2.97	p = .119
Undefined	4.70 ± 1.27
Education
Secondary school degree	4.93 ± 3.44
High school degree	5.77 ± 2.82	df = 4
Specialized secondary education	4.31 ± 2.53	p < .001
Bachelor’s degree	3.92 ± 2.85
Master’s degree or above	6.44 ± 2.84
Employment status
Full-time employment	6.07 ± 2.78
Part-time employment	5.52 ± 2.71	df = 5
Self-employed	4.32 ± 2.54	p < .001
Unemployed	3.48 ± 2.67
Student	7.31 ± 2.88
Retired	6.35 ± 2.00
Environment
Not a concern	4.07 ± 2.80
Industrial environment/concern	4.21 ± 2.56	df = 5
Urban environment/concern	6.33 ± 3.13	p < .001
Another environment/concern	6.24 ± 3.13
Not working	4.37 ± 3.01
Home environment	4.49 ± 2.52
Average monthly household income
<100,000 KZT	3.58 ± 2.23
100,000–250,000 KZT	4.34 ± 3.03	df = 4
250,001–500,000 KZT	5.30 ± 2.98	p < .001
500,001–1,000,000 KZT	6.16 ± 2.77
>1,000,000 KZT	6.54 ± 2.84
Health status
High blood pressure
Yes	4.19 ± 2.72	df = 1
No	4.56 ± 2.92	p < .128
Cardiovascular conditions
Yes	4.33 ± 2.01	df = 1
No	4.48 ± 2.90	p = .234
Diabetes
Yes	3.53 ± 2.59	df = 1
No	4.48 ± 2.88	p = .351
Asthma
Yes	7.20 ± 3.84	df = 1
No	4.43 ± 2.85	p = .002
COPD
Yes	6.10 ± 3.45	df = 1
No	4.45 ± 2.87	p = .05

Figure 2.

Sources of Air Pollution Perceived by Participants.

Figure 3.

Sources of Information about Air Pollution-related Topics Among the Participants.

Students, participants who worked in urban environments and those with higher economic status (i.e., average monthly household income>1 million KZT) showed a higher level of knowledge compared to the rest of the population (Table 8). Notably, participants diagnosed with asthma had the highest average knowledge score (mean = 7.20 ± 3.84 [p = .002]).

Attitude Towards Environmental Protection and WTP

Most participants were largely in agreement with the statements regarding environmental protection, (mean score > 3.00 [neutral response]) (Figure 4). Almost 30% of the respondents prioritized environmental protection over economic growth. The strongest agreement was noted for statements about the importance of environmental literacy (mean = 3.34): ‘Educating younger generations about the knowledge of environmental protection is important;’ and general attitude towards environmental protection (mean = 3.30): ‘Taking care of the environment is something I really care about.’

Opinions against environmental protection were mostly neutral or supportive (mean > 3.20). Almost 42% of the study population supported the statement ‘The economic growth of Kazakhstan is currently more important than environmental protection.’ Almost half of the respondents (43.2%) were sceptical about the individual actions that can be taken to improve air quality: ‘Nothing can be done by me or my family or friends to improve the atmospheric situation.’ One-third of the participants expressed a lack of motivation towards individual actions unless the initiative is supported in the community: ‘There is no point in doing what I can for the environment unless everyone does the same.’

Most respondents either demonstrated a WTP for environmental protection or remained neutral in their WTP statements (Figure 4). Half of the respondents (51.4%) supported the idea of governmental intervention for environmental protection without self-involvement (mean = 3.34): ‘Kazakhstan’s government has to reduce atmospheric pollution, but it should not incur any costs to me.’ Only 32% of the surveyed population expressed their WTP extra taxes for enhancing air quality (mean = 2.96): ‘I do not object to a tax increase if the additional funds are utilised to prevent further atmospheric pollution.’ Furthermore, 36.0% of participants expressed their preparedness to accept a reduction in their participants expressed their preparedness to accept a reduction in their standard of living to protect the environment.

Figure 4.

Respondents’ Opinions For (a) and Against (b) Environmental Protection, and (c) WTP for Environmental Protection.

Discussion

The results of the SEM analysis highlight the significance of local air quality knowledge and its effect on the public perception of national air pollution and the economy. This resonates with other studies where knowledge and education are prime factors in improving environmental attitudes (Meinhold & Malkus, 2005; Sudarmadi et al., 2001). It suggests that increasing environmental literacy among the general public should be considered a priority to promote pro-environmental behaviour. In addition, since almost all respondents use online resources for air pollution-related information, it is crucial to disseminate knowledge of air pollution and related health effects through easily accessible means (e.g., social media) to benefit a population with varying socio-economic backgrounds (Finn & O’Fallon, 2017; Yang & Wu, 2019).

The bivariate analysis further supported the impact of participants’ knowledge on their environmental attitudes and perceptions. Similar to the present study, Hou et al. (2021) also noted moderate air pollution health literacy, linked to factors such as education and living arrangement. In Accra, Ghana, Odonkor and Mahami (2020) found lower literacy among the elderly and less educated population. Graduates exhibited higher environmental knowledge, emphasizing the role of education. The highest level of environmental knowledge was evident for participants with a graduate degree, suggesting formal education is a leading factor in promoting environmental awareness. In Kazakhstan, despite 51.7% of employed individuals holding bachelor’s degrees (Bureau of National Statistics, 2022), the majority still display low environmental literacy levels. Interestingly, countries such as Germany and Sweden, with lower bachelor’s degree percentages, exhibit higher environmental awareness (Grund & Brock, 2020). In China, where tertiary education reaches only 10% of the population aged 25–64 (OECD, 2023), environmental education is mandatory in primary and secondary schools (Wu, 2012). A study on Chinese students and adults by Clayton et al. (2019) found moderate environmental knowledge levels and a strong intention for pro-environmental behaviour, suggesting additional factors beyond institutional knowledge influencing environmental literacy.

Low environmental knowledge may explain Astana residents’ perception of air quality as non-threatening. Despite being the most polluted cities in Kazakhstan by API ranking with substantial regional variations in the concentration of airborne pollutants, public awareness remains low (Kerimray et al., 2018). Increased coal combustion in CHPPs and households during winter degrades air quality substantially (The World Bank, 2021). Astana’s CHPPs consume 3.2 million tonnes of coal annually, and two-thirds of Kazakhstani households still use solid fuels (e.g., coal, biomass and wood) for heating.

Promoting pro-environmental behaviour involves putting more emphasis on associated health hazards and individual intentions (Bamberg & Möser, 2007). Personal self-consciousness and identity influence intention for actions (Huang et al., 2020). Individuals identifying as environmentalists are more inclined to act (Carfora et al., 2017; Tabernero et al., 2015). Moreover, a perceived lack of control over environmental issues hinders individual actions and WTP for environmental protection (e.g., environmental taxes or higher-quality gasoline) (Vicente et al., 2021).

Developing countries may prioritize survival over environmental concerns, yet studies show no income–WTP link. Moreover, research also suggests that higher pollution levels could increase environmental concerns (Dunlap & York, 2016; Shao et al., 2018). In the present study, SEM analysis found a link between WTP and environmental attitude, suggesting spending on environmental protection influences attitudes and perceived control. This underscores the impact of perceived control on behaviour intentions and attitudes towards pro-environmental actions. Bivariate analysis aligns with SEM findings, as most respondents expressed WTP or neutrality.

One form of payment for environmental preservation is environmental taxation. New environmental regulations introduced penalties and fees to minimize non-compliance with emission standards in Kazakhstan. However, the emission limit values (ELVs) established for industrial plants in the country are higher than those recommended by international guidelines (e.g., the EU Industrial Emissions Directive) (Assanov et al., 2021). Moreover, environmental sanctions are more directed towards government budget revenue rather than towards environmental protection (Abakhanov, 2020). However, the Kazakhstani Environmental Code, implemented in 2021, introduced a payment for negative environmental impact (NEI) for large corporations, that gradually increases every three years. NEI assessment for stationary sources depends on emission concentrations. Stationary sources with emissions higher than ELVs are subjected to administrative penalties and environmental damage payments (The World Bank, 2021). Around 40% of respondents favour higher-quality fuel, and WTP more for reduced pollution levels. Mobile source pollution tax considers engine size and fuel type e.g., unleaded gasoline, diesel fuels, compressed gas), varying between summer and winter periods (The World Bank, 2021).

Limitations and Recommendations

The present study has several limitations: While the sample size was adequate for the current study objectives, a larger and more diverse sample covering a broader population can provide more robust and generalizable results. Moreover, with a smaller sample size, SEM may tend to overestimate the relationships among variables. Although the study population encompassed individuals from diverse socio-economic backgrounds, the present study solely focused on urban residents, with the majority having attained tertiary education. Furthermore, the responses from participants in the present study may not accurately represent the overall perception of air pollution in the country, as there are other cities in Kazakhstan that the general public associates with higher levels of air pollution. The section of the survey instrument addressing the WTP should incorporate open-ended questions to explore the reasons behind participants’ reluctance to invest in environmental protection measures. This approach will facilitate a more thorough understanding of public attitudes towards air pollution.

Conclusion

The present study assessed the perception, attitude, and knowledge of the local air quality among adult urban residents of Astana, a city with a population boom situated in Kazakhstan which is a developing economy. SEM was the tool used to investigate the causal relationship between perceived air quality, knowledge of air pollution-related topics and WTP for environmental protection:

SEM highlights knowledge as a crucial factor for improving awareness and perception of national air pollution levels.

Prioritizing online channels for public education, especially via social media, is vital for effective air pollution mitigation.

Individuals with low environmental literacy tend to underestimate Astana’s pollution levels, while industrial workers perceive higher pollution and health risks.

Overall, there’s intermediate-level agreement among population groups on environmental protection and WTP.

Despite limited support for financial measures, SEM indicates that increased taxes can increase environmental protection attitudes.

Supplementary Material

Supplementary Material for this editorial is available online.

Footnotes

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Declaration of Conflicting Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Ethics Approval

The present study was approved by the appropriate institutional research ethics committee (Nazarbayev University Institutional Research Ethics Committee (NU IREC)).

Funding

The authors acknowledge the financial support from the Nazarbayev University Faculty Development Competitive Research Grant Program (Funder Project Reference: 280720FD1904).

References

Abakhanov

(2020). New environmental code of Kazakhstan: Expectations and prospects. Central Asian Bureau for Analytical Reporting. https://cabar.asia/en/new-environmental-code-of-kazakhstan-expectations-and-prospects

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. 10.1016/0749-5978(91)90020-t

Akhtar

, Saleem

, Nadeem

V. M.

, Shahid

, & Ikram

(2017). Assessment of willingness to pay for improved air quality using contingent valuation method. Global Journal of Environmental Science and Management, 3(3), 279–286.

Arslan

(2012). The influence of environment education on critical thinking and environmental attitude. Procedia-Social and Behavioral Sciences, 55, 902–909.

Assanov

, Zapasnyi

, & Kerimray

(2021). Air quality and industrial emissions in the cities of Kazakhstan. Atmosphere, 12(3), 314. 10.3390/atmos12030314

Aztatzi-Aguilar

, Valdés-Arzate

, Debray-García

, Calderón-Aranda

, Uribe-Ramirez

, Acosta-Saavedra

, Gonsebatt

, Maciel-Ruiz

, Petrosyan

, Mugica-Alvarez

, Gutiérrez-Ruiz

, Gómez-Quiroz

, Osornio-Vargas

, Froines

, Kleinman

, & De Vizcaya-Ruiz

(2018). Exposure to ambient particulate matter induces oxidative stress in lung and aorta in a size- and time-dependent manner in rats. Toxicology Research and Application, 2. 10.1177/2397847318794859

Bamberg

, & Möser

(2007). Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1), 14–25. 10.1016/j.jenvp.2006.12.002

Bickerstaff

, & Walker

(2001). Public understandings of air pollution: the ‘localisation’of environmental risk. Global Environmental Change, 11(2), 133–145.

Carducci

, Fiore

, Azara

, Bonaccorsi

, Bortoletto

, Caggiano

, Calamusa

, De Donno

, De Giglio

, Dettori

, Di Giovanni

, Di Pietro

, Facciolà

, Federigi

, Grappasonni

, Izzotti

, Libralato

, Lorini

, Montagna

, … Ferrante

(2021). Pro-environmental behaviors: Determinants and obstacles among Italian university students. International Journal of Environmental Research and Public Health, 18(6), 3306. 10.3390/ijerph18063306

10.

Carfora

, Caso

, Sparks

, & Conner

(2017). Moderating effects of pro-environmental self-identity on pro-environmental intentions and behavior: A multi-behavior study. Journal of Environmental Psychology, 53, 92–99. 10.1016/j.jenvp.2017.07.001

11.

Carlsson

, & Johansson-Stenman

(2000). Willingness to pay for improved air quality in Sweden. Applied Economics, 32(6), 661–669.

12.

Chin

, De Pretto

, Thuppil

, & Ashfold

(2019). Public awareness and support for environmental protection: A focus on air pollution in peninsular Malaysia. PLoS ONE, 14(3). 10.1371/journal.pone.0212206

13.

Clayton

, Bexell

, Xu

, Tang

, Li

, & Chen

(2019). Environmental literacy and nature experience in Chengdu, China. Environmental Education Research, 25(7), 1105–1118. 10.1080/13504622.2019.1569207

14.

Dunlap

, & York

(2016). The globalization of environmental concern and the limits of the postmaterialist values explanation: Evidence from four multinational surveys. The Sociological Quarterly, 49(3), 529–563. 10.1111/j.1533-8525.2008.00127.x

15.

Erhabor

N. I.

, & Don

J. U.

(2016). Impact of environmental education on the knowledge and attitude of students towards the environment. International Journal of Environmental and Science Education, 11(12), 5367–5375.

16.

Farina

, Sancini

, Mantecca

, Gallinotti

, Camatini

, & Palestini

(2011). The acute toxic effects of particulate matter in mouse lung are related to size and season of collection. Toxicology Letters, 202(3), 209–217. 10.1016/j.toxlet.2011.01.031

17.

Finn

, & O’Fallon

(2017). The emergence of environmental health literacy–from its roots to its potential. Environ Health Perspectives, 125(4). 10.1289/ehp.1409337

18.

Grund

, & Brock

(2020). Education for sustainable development in Germany: Not just desired but also effective for transformative action. Sustainability, 12(7), 2838. 10.3390/su12072838

19.

Gupta

(2018). Effectiveness of air pollution treaties: The role of knowledge, power, and participation. In knowledge, power, and participation in environmental policy analysis (pp. 145–174). Routledge.

20.

Hair

, Hult

, Ringle

, & Sarstedt

(2018). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, Inc. https://www.pls-sem.net/pls-sem-books/advanced-issues-in-pls-sem/

21.

Hair Jr.

J. F.

, Matthews

L. M.

, Matthews

R. L.

, & Sarstedt

(2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

22.

Hanley

, Barbier

E. B.

, & Barbier

(2009). Pricing nature: Cost–benefit analysis and environmental policy. Edward Elgar Publishing.

23.

Hou

, Huang

, Lu

, Chen

, Lee

, Lin

, Wang

, Sulistyorini

, & Li

(2021). A national survey of ambient air pollution health literacy among adult residents of Taiwan. BMC Public Health, 21, 1604. 10.1186/s12889-021-11658-z

24.

Huang

, Ou

, & Gao

(2020). Nationwide response to air pollution: Environmental behavior model based on self-identity and social identity process. The International Journal of Electrical Engineering & Education, 0(0). 10.1177/0020720920940604

25.

Hungerford

, & Volk

(1990). Changing learner behavior through environmental education. The Journal of Environmental Education, 21(3), 8–21. 10.1080/00958964.1990.10753743

26.

Janjua

, Powell

, Atkinson

, Stovold

, & Fortescue

(2019). Individual-level interventions to reduce personal exposure to outdoor air pollution and their effects on long-term respiratory conditions. Cochrane Database of Systematic Reviews, 8(8). 10.1002/14651858.cd013441

27.

Kelly

, & Fussell

(2012). Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmospheric Environment, 60, 504–526. 10.1016/j.atmosenv.2012.06.039

28.

Kelly

F. J.

, & Fussell

J. C.

(2015). Air pollution and public health: Emerging hazards and improved understanding of risk. Environmental Geochemistry and Health, 37, 631–649.

29.

Keller

, Bostrom

, Kuttschreuter

, Savadori

, Spence

, & White

(2012). Bringing appraisal theory to environmental risk perception: A review of conceptual approaches of the past 40 years and suggestions for future research. Journal of Risk Research, 15(3), 237–256. 10.1080/13669877.2011.634523

30.

Kerimray

, Bakdolotov

, Sarbassov

, Inglezakis

, & Poulopoulos

(2018). Air pollution in Astana: Analysis of recent trends and air quality monitoring system. Materials Today: Proceedings, 5(11), 22749–22758. 10.1016/j.matpr.2018.07.086

31.

Kollmuss

, & Agyeman

(2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research, 8(3), 239–260.

32.

Laumbach

, Meng

, & Kipen

(2015). What can individuals do to reduce personal health risks from air pollution? Journal of Thoracic Disease, 7(1), 96–107. 10.3978/j.issn.2072-1439.2014.12.21

33.

Lee

, Petter

, Fayard

, & Robinson

(2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305–328.

34.

, & Hu

(2018). Perceived health risk, environmental knowledge, and contingent valuation for improving air quality: New evidence from the Jinchuan mining area in China. Economics & Human Biology, 31, 54–68. 10.1016/j.ehb.2018.07.007

35.

Loopmans

, Smits

, & Kenis

(2022). Rethinking environmental justice: Capability building, public knowledge and the struggle against traffic-related air pollution. Environment and Planning C: Politics and Space, 40(3), 705–723.

36.

Maione

, Mocca

, Eisfeld

, Kazepov

, & Fuzzi

(2021). Public perception of air pollution sources across Europe. Ambio, 50(6), 1150–1158. 10.1007/s13280-020-01450-5

37.

Meinhold

, & Malkus

(2005). Adolescent environmental behaviors. Environment and Behavior, 37(4), 511–532. 10.1177/0013916504269665

38.

Milfont

(2007). Psychology of environmental attitudes: A cross-cultural study of their content and structure. Unpublished doctoral dissertation, University of Auckland, Auckland, New Zealand.

39.

Milfont

, & Duckitt

(2010). The environmental attitudes inventory: A valid and reliable measure to assess the structure of environmental attitudes. Journal of Environmental Psychology, 30(1), 80–94. 10.1016/j.jenvp.2009.09.001

40.

National Bureau of Statistics and agency for strategic planning and reforms of the Republic of Kazakhstan. (2022). Demographic Statistics. The Agency for strategic planning and reforms of the Republic of Kazakhstan. https://taldau.stat.gov.kz/ru/NewIndex/GetIndex/703831?keyword=.

41.

Odonkor

, & Mahami

(2020). Knowledge, attitudes, and perceptions of air pollution in Accra, A critical survey. Journal of Environmental and Public Health, 2020. 10.1155/2020/3657161

42.

OECD. (2023). China: Overview of the education system (EAG 2022). Education GPS–China–Overview of the education system (EAG 2022). https://gpseducation.oecd.org/CountryProfile?primaryCountry=CHN&treshold=10&topic=EO#:~:text=Among%2025%2D64%20year%2Dolds,doctoral%20degrees%20combined%20with%201%25

43.

Qian

, Xu

, Li

, Shen

, He

, Liang

, Yang

, Zhou

, & Xu

(2016). Knowledge and perceptions of air pollution in Ningbo, China. BMC Public Health, 16(1). 10.1186/s12889-016-3788-0

44.

Rajper

, Ullah

, & Li

(2018). Exposure to air pollution and self-reposted effects on Chinese students: A case study of 13 megacities. PloS ONE, 13(3). 10.1371/journal.pone.0194364

45.

Ramírez

, Ramondt

, Van Bogart

, & Perez-Zuniga

(2019). Public awareness of air pollution and health threats: Challenges and opportunities for communication strategies to improve environmental health literacy. Journal of Health Communication, 24(1), 75–83. 10.1080/10810730.2019.1574320

46.

Saari

, Damberg

, Frömbling

, & Ringle

(2021). Sustainable consumption behavior of Europeans: The influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecological Economics, 189. 10.1016/j.ecolecon.2021.107155

47.

Saksena

(2012). Public perceptions of urban air pollution risks. Risk, Hazards and Crisis in Public Policy, 2(1), 19–37. 10.2202/1944-4079.1075

48.

Shao

, Tian

, & Fan

(2018). Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China. World Development, 105, 83–94. 10.1016/j.worlddev.2017.12.033

49.

Steg

, Bolderdijk

., Keizer

, & Perlaviciute

(2014). An integrated framework for encouraging pro-environmental behaviour: The role of values, situational factors and goals. Journal of Environmental Psychology, 38, 104–115. 10.1016/j.jenvp.2014.01.002

50.

Sudarmadi

, Suzuki

, Kawada

, Netti

, Soemantri

, & Tri Tugaswati

(2001). A Survey of perception, knowledge, awareness, and attitude in regard to environmental problems in a sample of two different social groups in Jakarta, Indonesia. Environment, Development and Sustainability, 3(2), 169–183. 10.1023/a:1011633729185

51.

Tabernero

, Hernández

, Cuadrado

, Luque

, Pereira

(2015). A multilevel perspective to explain recycling behaviour in communities. Journal of Environmental Management, 159, 192–201. 10.1016/j.jenvman.2015.05.024

52.

Vassanadumrongdee

, & Matsuoka

(2005). Risk perceptions and value of a statistical life for air pollution and traffic accidents: Evidence from Bangkok, Thailand. Journal of Risk and Uncertainty, 30(3), 261–287. 10.1007/s11166-005-1155-0

53.

Vicente

, Marques

, & Reis

(2021). Willingness to pay for environmental quality: The effects of pro-environmental behavior, perceived behavior control, environmental activism, and educational level. Sage Open, 11(4). 10.1177/21582440211025256

54.

Voorhees

S. S.

, Sakai

, Araki

, Sato

, & Otsu

(2001). Cost-benefit analysis methods for assessing air pollution control programs in urban environments: A review. Environmental Health and Preventive Medicine, 6, 63–73.

55.

Wang

, Yang

, Chen

, Kan

, Wu

, Wang

, Maddock

, & Lu

(2015). Knowledge, attitudes, and practices (KAP) of the relationship between air pollution and children’s respiratory health in Shanghai, China. International Journal of Environmental Research and Public Health, 12(2), 1834–1848. 10.3390/ijerph120201834

56.

World Bank. (2021). Cost-effective air quality management in Kazakhstan and its impact on greenhouse gas emissions, (Report No. AUS0002588). International Bank for Reconstruction and Development. https://documents1.worldbank.org/curated/en/099345012232191779/pdf/P1708700d2bd3a09093fa0cd27991d0662.pdf

57.

(2012). Exploring the new ecological paradigm scale for gauging children’s environmental attitudes in China. The Journal of Environmental Education, 43(2), 107–120. 10.1080/00958964.2011.616554

58.

Xue

, Hu

, Quiros

, Ayala

, & Jung

(2019). How do particle number, surface area, and mass correlate with toxicity of diesel particle emissions as measured in chemical and cellular assays? Chemosphere, 229, 559–569. 10.1016/j.chemosphere.2019.05.010

59.

Yang

, & Wu

(2019). How social media exposure to health information influences Chinese people’s health-protective behavior during air pollution: A theory of planned behavior perspective. Health Communication, 36(3), 324–333. 10.1080/10410236.2019.1692486

Understanding Public Perception of Air Quality in the Urban Environment of Central Asia: An Empirical Assessment Using Structural Equation Modelling

Abstract

Keywords

Introduction

Background

Structural Equation Modelling

Environmental Attitude

Perception of National Air Pollution and Economy

Willingness to Pay

Institutional Knowledge

Knowledge of Local Air Quality

Methodology

Study Area

Instrumentation

Data Collection

Statistical Analysis

SEM and Model Validity

Proposed SEM Model.

Results

Socio-demographic Characteristics of Studied Population

SEM Validity Check

Relationship Between Air Quality Perception and Socio-demographics

Respondents’ Knowledge of Air Pollution-related Topics

Sources of Air Pollution Perceived by Participants.

Sources of Information about Air Pollution-related Topics Among the Participants.

Attitude Towards Environmental Protection and WTP

Respondents’ Opinions For (a) and Against (b) Environmental Protection, and (c) WTP for Environmental Protection.

Discussion

Limitations and Recommendations

Conclusion

Supplementary Material

Footnotes

Consent to Participate

Data Availability Statement

Declaration of Conflicting Interests

Ethics Approval

Funding

References