https://orcid.org/0009-0004-6623-7665
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Abstract
The effect of climate change is visible in the Dhaka Metropolitan area. This study aimed to examine the factors associated with climate change knowledge, attitudes, risk perception, and health-related adaptation behaviors, and to assess their interrelationships. This cross-sectional study was conducted in September 2025 among 411 individuals from the Dhaka Metropolitan area. The data were collected through face-to-face interviews with a random sampling technique. The questionnaire had five sections, including demographic characteristics, knowledge, attitudes, risk perception, and adaptation behavior. Simple and multiple linear regression were performed to examine the associated factors, while Pearson correlation was used to explore the relationships among knowledge, attitudes, risk perception, and adaptation. Results showed that the level of education was associated with knowledge, attitude, risk perception, and adaptation behavior. Also, monthly income was associated with adaptation behavior, while cardiovascular disease was associated with knowledge and adaptation behavior. Additionally, knowledge, attitude, and risk perception were positively correlated, while knowledge and adaptation were negatively correlated. The findings underscore the need to strengthen climate change education, particularly for individuals with lower educational attainment, integrate health-focused adaptation strategies for vulnerable groups such as those with cardiovascular disease, provide socioeconomic support to enhance adaptive capacity, and implement community-based programs that help translate knowledge, attitudes, and risk perceptions into effective adaptive behaviours in the Dhaka Metropolitan population.
Citation: Sakib MN, Ahmed I, Riad NI, Rahman MM (2026) Climate change knowledge, attitude, risk perception, and health adaptation behavior in an urban megacity. PLOS Clim 5(4): e0000901. https://doi.org/10.1371/journal.pclm.0000901
Editor: Teodoro Georgiadis, Institute for BioEconomy CNR, ITALY
Received: December 1, 2025; Accepted: March 30, 2026; Published: April 20, 2026
Copyright: © 2026 Sakib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information file.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Climate change is defined as a shift in the climate that lasts for a long time, usually decades or more, and is characterized by variations in the mean and/or variability of its properties (such as temperature, precipitation, humidity, incident radiation, and wind patterns) [1]. According to IPCC, depending on the quantity of greenhouse gases released, the Earth’s mean surface air temperature has risen from 0.3 to around 0.6˚C during the last century and might rise from 1.4 to 5.8˚C over the next [2]. The production and consumption of energy, the availability of food and water, human health, socioeconomics, lifestyles, governance, political stability, international trade, and migration are all negatively affected by and will continue to be negatively affected by climate change [3].
Climate change knowledge refers to an individual’s cognitive understanding of the reality or facts about climate change [4]. Attitude refers to a psychological tendency or evaluative disposition developed through experience, which influences individuals’ responses and behaviors toward a given issue [5], whereas climate change perception is the process by which information about the topic is absorbed and converted into psychological awareness, i.e., how people perceive and evaluate climate change in all its aspects [4]. Climate change adaptation is the adjustment to anthropogenic climate change that seeks to minimize losses and maximize benefits to maintain system viability [6]. Climate change knowledge, attitude, and risk perception are positively correlated [7–10]. But behavioral change is not always easy. Knowledge, attitude, and perceived risk may not always translate into behavioral change [11,12], as they can be influenced by other factors or have interrelations [10]. For instance, sociodemographic factors, such as family size, low economic status, education, and gender, may also affect adaptation behavior [13–15].
Climate change has been significantly affecting the environment, agriculture, water resources, and food security [16,17]. Additionally, massive infrastructure damage, supply chain disruption, and increased poverty levels are caused by climate-related disasters in vulnerable regions [18]. Moreover, it is estimated that the economic losses from climate change could range from $140 billion to $300 billion annually by 2030, and may increase to $280 billion to $500 billion annually by 2050 [19]. Furthermore, by altering vector habitats and transmission dynamics, climate change affects the spread of infectious diseases and complicates disease surveillance and management [18]. Climate change is predicted to cause an additional 250,000 fatalities per year from malnutrition, malaria, diarrhea, and heat stress alone between 2030 and 2050 [20].
In Bangladesh, the frequency and severity of extreme weather events have significantly increased in recent decades [21]. The average temperature has been increasing by 0.20 degrees Celsius every decade [22]. Additionally, previous studies have revealed a rising tendency for three seasons, with the exception of winter, which is becoming cooler and drier, and the rest of the year being warmer and wetter [23–25]. Furthermore, climate change enhances the prevalence of uncommon diseases in Bangladesh [26]. For instance, a large numbers of people is affected by heatstroke, cardiovascular disease, respiratory illness, and allergies, particularly in urban areas [27]. However, Dhaka is one of the most climate change-affected megacities in the Global South [28]. Megacities are defined as metropolitan regions with a population of five million or more, where the population of the capital of Bangladesh, Dhaka, is around 15 million and has a 3.6% annual growth rate, one of the largest in the world [29,30]. A significant portion of Dhaka’s growth is driven by climate pressures, as migrants relocate to the city from rural and coastal regions that have experienced catastrophic occurrences like cyclones or rapid land erosion [31]. Adaptation measures will be needed to achieve resilience to the impacts of climate variability in this highly exposed, poorly prepared city [32]. While there is much evidence that Dhaka and other megacities in the Global South are vulnerable, less focus has been placed on how governments, business entities, and international organizations intend to adapt to climate change and improve local populations’ resilience [30]. Also, no prior study has examined the factors associated with climate change knowledge, attitudes, risk perception, and health-related adaptation behaviors among residents of Dhaka. Considering the gap, this study aimed to examine the factors associated with climate change knowledge, attitudes, risk perception, and health-related adaptation behavior, as well as their interconnection among the residents of the Dhaka Metropolitan Area. Specifically, the objectives of this study are to (i) assess variations in climate change knowledge, attitudes, risk perception, and health-related adaptation behaviors across sociodemographic and health characteristics; (ii) examine the determinants of adaptation behaviors among urban residents; and (iii) identify the interrelationships among knowledge, attitudes, risk perception, and adaptation behaviors. We assumed that climate change knowledge, attitudes, risk perception, and health-related adaptation behavior are interconnected. It is expected that this study will help policymakers and urban planners in climate change adaptation and mitigation planning.
2. Behavioral theories and models
Understanding behavioral responses to climate‑related health risks requires well‑established theoretical guidance. Several models have been widely applied in health and environmental behavior research.
2.1. Knowledge, Attitude, and Practice (KAP) Model
The KAP model posits that an individual’s knowledge about a particular issue influences their attitudes, which, in turn, shape their behaviors. It has been widely applied in research on climate change knowledge, attitudes, and practices among populations, as well as on their implications for adaptation behaviors. For example, a systematic review summarizing KAP research toward climate change illustrates how knowledge and attitudes are linked to behavioral responses [33].
2.2. Health Belief Model (HBM)
The HBM emphasizes individual perceptions as predictors of behavior. Its core components, perceived susceptibility, perceived severity, perceived benefits, and perceived barriers, help explain why individuals adopt or fail to adopt adaptation measures. Recent studies have applied extended HBM frameworks to examine how beliefs about climate impacts influence health-related behavioral intentions, highlighting their relevance for climate‑health adaptation research [34].
2.3. Theory of Planned Behavior (TPB)
TPB extends beyond attitudes to include subjective norms and perceived behavioral control as determinants of behavioral intentions. Empirical studies have applied TPB in climate adaptation and pro‑environmental behavior contexts, demonstrating that attitudes, norms, and perceived control significantly influence adaptation intentions [35].
2.4. Protection Motivation Theory (PMT)
PMT focuses on how perceived threat and coping efficacy motivate protective behavior. Although originally developed to address health threats, it has been widely adopted to investigate adaptive and mitigation behaviors related to environmental risks, including climate change, by linking threat appraisal and coping responses to intentional behaviors [36].
3. Methodology
3.1. Ethics statement
All procedures in this study adhered to the ethical standards established in the Declaration of Helsinki and its subsequent revisions for research involving human subjects [37]. The Research Ethical Committee at Bangladesh University of Professionals approved the study protocol (Reference number: 23.0.902.858.07.786.24/46). Verbal informed consent was obtained from each participant. The consent process included a clear explanation of the study objectives, procedures, the voluntary nature of participation, confidentiality, and the right to withdraw at any time without consequence. Consent was documented electronically in KoboToolbox, where the first question asked participants to confirm their agreement to participate; responses to this question were not included in the final dataset. Minors were not included in this study. All participants were adults aged 18 years or older; therefore, parental or guardian consent was not required. The data were collected anonymously, and no identifying numbers or images of the participants were collected.
3.2. Study area
The Dhaka Metropolitan Area (DMA) was selected as the study area for research (Fig 1). DMA, which is situated on the eastern bank of the Buriganga River. The coordinates of DMA are 90240” East longitude and 23430” North latitude. Many people live in slums, and squatters have very limited access to urban services, including waste disposal, potable water, a reliable electricity supply, and sanitary facilities [38]. Also, between 2000 and 2020, the DMA’s urban areas expanded by 20.52% [39]. The Land Surface Temperature (LST), on the other hand, increased by 2 °C between 1990 and 2011 [40].
AreGIS 10.8 was used for this purpose. No third-party permission is required to publish it).
3.3. Study design and sampling
This cross-sectional study was conducted in September 2025. Four Thanas (local administrative units in Bangladesh, similar to sub-districts or police precincts) were selected from the Dhaka Metropolitan Area, including Mirpur, Pallabi, Tejgaon, and Motijheel. These Thanas were purposively selected because previous research identified them as highly temperature-prone areas, making them vulnerable to heat-related impacts of climate change [41]. Each Thana was divided into several sections or blocks. Using a lottery method, a few sections or blocks were randomly selected from each Thana. Within the selected sections or blocks, all individuals who were available and met the inclusion criteria were approached for face-to-face interviews. Data were collected by the authors using KoboToolbox. The following eligibility criteria were considered when recruiting the participants: (i) being Bangladeshi by birth; (ii) age above 18 years; and (iii) living in the Dhaka Metropolitan Area for at least two years. Yamane’s formula [42] was used to calculate the sample size:
where n = sample size, N = population (According to a recent report, 22,478,116 people live in the Dhaka Metropolitan Area) [43], and e = error tolerance.
Therefore, the estimated sample size was 400 (95% confidence interval). Finally, 411 participants were recruited for the final analysis.
3.4. Questionnaire and measurement
The questionnaire used in this study was adapted from a previous study [10]. The questionnaire includes five sections: sociodemographic, knowledge, attitudes, risk perception, and adaptation behaviors. A pilot survey was conducted among 15 participants prior to the main study to assess the clarity, relevance, and feasibility of the questionnaire. However, the data from the pilot survey were not included in the final analysis. During the reliability assessment of the final dataset, the 3rd and 4th items of the knowledge section, the 5th item of the risk perception section, and the 1st, 2nd, and 8th items demonstrated low internal consistency. Consequently, these items were removed to improve the overall reliability of the respective scales. The final statistical analyses were conducted using the revised questionnaire after excluding these items.
3.4.1. Sociodemographic information.
Sociodemographic variables include the study, age (18–34/35–54/54 or more), sex (male/female), marital status (married/unmarried), level of education (basic or elementary/secondary or higher secondary/ graduate or others), employment status (employed/unemployed), family type (nuclear/large), family history of cardiovascular problem (yes/no), self-cardiovascular problems (yes/no). Monthly household income was categorized into three groups: low (<20,000 BDT), middle (20,000–50,000 BDT), and high (>50,000 BDT), based on contextual income distribution and cost-of-living considerations in Bangladesh. Minimum wages in Bangladesh vary by sector, with the ready-made garment industry reporting approximately 12,500 BDT/month following the 2023 revision (previously 8,000 BDT) [44]. However, existing evidence suggests that such wages are often insufficient to meet basic living costs in urban areas, where estimates indicate that at least 17,000–23,000 BDT/month may be required for a basic standard of living [45].
3.4.2. Knowledge.
Knowledge was assessed with the five statements. Responses of “True” or the selection of multiple-choice questions in impact and mitigation were assigned “1” while responses of “False” or “None of them” were assigned “0”. The possible minimum and maximum scores range from 0 to 18; the higher score indicates better knowledge. The Cronbach’s α value of the knowledge section in the current study was 0.74.
3.4.3. Attitude.
Attitude was assessed with the 4 statements, all of which were positively worded. Participants indicate their level of agreement with each statement expressed on the 5-point Likert scale, ranging from “strongly disagree” to “strongly agree”. For calculation, 1 = “strongly disagree”, 2 = “disagree”, 3 = “neutral”, 4 = “agree”, and 5 = “strongly agree” were assigned. The possible minimum and maximum scores range from 5 to 20. The Cronbach’s α value of the attitude section in the current study was 0.77.
3.4.4. Risk perception.
Perceived risk was evaluated using five statements, with the 5-point Likert scale ranging from “strongly disagree” to “strongly agree”. For calculation, 1 = “strongly disagree”, 2 = “disagree”, 3 = “neutral”, 4 = “agree”, and 5 = “strongly agree” were assigned. The possible minimum and maximum scores range from 5 to 25. The Cronbach’s α value of the risk perception section in the current study was 0.80.
3.4.5. Adaptation behavior.
The adaptation section includes five statements. For calculation, 6th negatively worded statement 1 = “9-10 times”, 2 = “7-8 times”, 3 = “4-6 times”, 4 = “2-3 times”, and 5 = “0-1 times” were assigned. In contrast, for the rest of the statements 1 = “0-1 times”, 2 = “2-3 times”, 3 = “4-6 times”, 4 = “7-8 times”, and 5 = “9-10 times” were assigned. The possible minimum and maximum scores range from 5 to 25. The Cronbach’s α value of the adaptation behavior section in the current study was 0.67.
3.5. Description of the study sample
Table 1 presents the sociodemographic information of the study participants. Among the 411 respondents, nearly half were aged 18–34 years (48%), followed by those aged 35–54 years (47%), while only 5% were aged 55 years or older. The majority were male (77%), and about one-fourth were female (23%). Most participants were married (70%), whereas 30% reported being unmarried. In terms of education, a large proportion had only basic/elementary schooling (71%), 20% had secondary or higher secondary education, and only 9% had attained higher education (bachelor’s degree or above). Regarding household income, over two-thirds (68%) reported a monthly family income below BDT 20,000, about one-third (31%) had an income between BDT 20,000–50,000, and only 1% reported earnings above BDT 50,000. The majority of respondents were employed (79%), with 21% unemployed. Nuclear families were more common (65%) compared to large families (35%). Concerning cardiovascular health, three-fourths (75%) reported no family history of cardiovascular disease, while 25% reported a positive family history. Self-reported cardiovascular problems were relatively uncommon, with 8% reporting such conditions compared to 92% who did not.
3.6. Statistical analysis
Data were checked for completeness, and questionnaires with substantial missing information were excluded. The initial data cleaning and processing were conducted using Microsoft Excel 2013. Descriptive and inferential analyses were conducted using R programming (version 4.3.2) [46], where data management and summarization were performed using the dplyr [47] and gtsummary [48] packages, regression diagnostics were done by lmtest [46], and data visualization was conducted using ggplot2 [49] packages. The study aimed to identify factors associated with climate change knowledge, attitudes, risk perceptions, and adaptation behaviours among residents of the Dhaka Metropolitan Area. The dependent variables were the scores for knowledge, attitude, perceived risk, and adaptation behavior, and the independent variables included age, sex, marital status, education level, family income, employment status, family type, and history of cardiovascular problems.
Simple linear regression was initially performed to examine unadjusted associations between each independent variable and the outcomes. Variables significant in the univariate analyses were included in multiple linear regression models to estimate adjusted associations. Linear regression was selected because the outcomes were continuous, allowing quantification of the strength and direction of associations while controlling for potential confounders.
Prior to conducting regression analyses, normality of residuals, homoscedasticity, and multicollinearity were assessed using standard diagnostic methods, including the Shapiro-Wilk test, the Breusch-Pagan test, and variance inflation factors (VIFs). All VIF values were < 2, indicating no multicollinearity concerns [50]. Although heteroskedasticity was observed, robust standard errors (HC1) were applied to account for it [51]. Furthermore, residuals deviated from normality (Shapiro-Wilk p < 0.05) [52]; however, given the large sample size, the regression estimates were considered robust [53]. Pearson correlation coefficients were calculated to examine relationships among continuous study variables. All statistical tests were two-sided, and p-value ≤ 0.05 were considered statistically significant. Regression results were reported with corresponding 95% confidence intervals (CIs).
4. Results
Table 2 shows the result of simple linear regression. Compared to participants aged 18–34 years, those aged 35–54 had lower knowledge scores (β = -1.3, 95% CI [-2.2, -0.33], p < .01), while participants aged 55 years and above had lower perception scores (β = -1.7, 95% CI [-3.20, -0.29], p < .05). Unmarried participants reported higher knowledge (β = 2.2, 95% CI [1.17, 3.28], p < .001) and perception (β = 0.82, 95% CI [0.30, 1.34], p < .01) compared to married participants. Higher education was strongly associated with greater knowledge (β = 6.9, 95% CI [5.67, 8.09], p < .001), attitude (β = 0.85, 95% CI [0.32, 1.38], p < .01), and perception (β = 2.7, 95% CI [2.09, 3.23], p < .001), while secondary education was also positively linked to knowledge (β = 3.4, 95% CI [2.24, 4.56], p < .001), perception (β = 0.64, 95% CI [0.03, 1.22], p < .05) and adaptation (β = -1.01, 95% CI [-2.01, -0.09], p < .05). Participants with higher income levels (>50,000 BDT) reported higher knowledge (β = 3.8, 95% CI [0.47, 7.10], p < .05) and adaptation (β = 2.6, 95% CI [2.22, 4.96], p < .01). Unemployed participants scored higher in knowledge (β = 3.6, 95% CI [2.26, 4.60], p < .001) and perception (β = 0.76, 95% CI [0.11, 1.40], p < .01) compared to employed participants. Moreover, a family history of cardiovascular disease was linked to higher knowledge (β = 2.0, 95% CI [0.90, 3.07], p < .001). Finally, participants with self-reported cardiovascular problems showed higher knowledge (β = 2.5, 95% CI [0.82, 4.13], p < .01) but lower adaptation scores (β = -2.4, 95% CI [-3.73, -1.12], p < .001).
The multivariate linear regression analysis identified several significant predictors of knowledge, attitude, perception, and adaptation (Table 3). Higher family income was linked to adaptation, as those earning above 50,000 BDT had significantly higher adaptation scores (β = 3.1, 95% CI [0.44, 5.82], p < .05). Higher education was strongly associated with greater knowledge (β = 6.0, 95% CI [4.31, 7.7], p < .001), attitude (β = 0.85, 95% CI [0.32, 1.3], p < .01), and perception (β = 2.6, 95% CI [1.85, 3.4], p < .001). Similarly, secondary education was positively associated with knowledge (β = 3.3, 95% CI [2.06, 4.4], p < .001) and adaptation (β = -1.12, 95% CI [-2.21, -0.18], p < .05). Finally, participants with self-reported cardiovascular problems reported higher knowledge (β = 2.1, 95% CI [0.13, 4.07], p < .05) but lower adaptation scores (β = -2.45, 95% CI [-3.73, -1.17], p < .05).
The correlation analysis revealed several significant associations (Table 4). Knowledge was positively correlated with attitude (r = .30, p < .001) and risk perception (r = .52, p < .001). Attitude was also positively correlated with risk perception (r = .42, p < .001). However, knowledge was negatively correlated with adaptation behavior (r = –.37, p < .001). No significant correlations were observed between adaptation and attitude (r = –.059, p > .05) or between adaptation and risk perception (r = –.077, p > .05).
Fig 2 presents a gender-wise comparison of climate change knowledge, attitude, risk perception, and adaptation using mean and standard deviation values. Overall, the mean scores are very similar across genders for all four domains. Knowledge scores were nearly identical between females (8.2 ± 5.4) and males (8.3 ± 4.5). Attitude and risk perception also showed minimal gender differences, with females reporting slightly higher attitude and risk perception scores than males. Adaptation scores were marginally higher among females (18.5 ± 4.3) compared to males (18.3 ± 3.6).
Participants reported varying levels of health-related adaptation behaviors in response to climate and weather conditions (Table 5). On hot days, respondents frequently wore hats or used sunshades (Mean = 3.59 ± 1.10), and on cold days, they often wore additional clothing to keep warm (Mean = 3.24 ± 1.09). Hygiene-related adaptation was the most frequently practiced, with washing hands before and after meals or toilet use showing the highest mean score (Mean = 4.23 ± 1.04). Participants also reported commonly drinking unboiled water or consuming raw foods (Mean = 3.55 ± 1.39). Preventive measures against vector-borne diseases were practiced at a moderate level: using mosquito nets or repellents during summer had a relatively high mean score (Mean = 3.71 ± 1.17).
5. Discussion
Understanding how climate change knowledge, attitudes, and risk perceptions shape health-related adaptation behaviours is essential for effective public health planning in urban areas. This study examined these factors among residents of the Dhaka Metropolitan Area, exploring variations across sociodemographic and health characteristics and their interrelationships. Climate change has been linked to a range of adverse health outcomes, including heat-related illness, cardiovascular and respiratory diseases, infectious disease outbreaks, and mental health impacts, especially among vulnerable urban populations [54]. Incorporating heat-related variables is important because exposure to extreme heat not only affects health outcomes directly but also influences individuals’ climate change knowledge, shapes attitudes toward environmental risks, heightens risk perception, and motivates adaptive behaviours to reduce vulnerability [55–57]. Examining these factors together allows for a more comprehensive understanding of how cognitive, affective, and health-related variables interact to influence adaptation behavior in urban populations.
This study found that higher educational attainment was positively associated with knowledge, attitudes, and risk perceptions of climate change. A prior study highlighted that final-year students had higher levels of knowledge, attitudes, and perceptions regarding climate change compared to juniors [18]. Education has repeatedly been identified as one of the most important predictors of people’s awareness about climate change [58–60]. A community‐based survey in Bangladesh found that higher educational attainment was significantly associated with greater awareness of climate change and its health impacts [61]. In addition, educated individuals are more likely to access and interpret information about environmental hazards, understand the scientific basis of climate change, and recognize implications for health and livelihoods, as higher education is linked with greater scientific knowledge and more nuanced attitudes toward climate change [62]. These consistent associations underscore the value of implementing climate change education programs across all segments of the population to enhance knowledge, foster positive attitudes, and elevate risk perceptions of climate change [63].
Surprisingly, we found that individuals with secondary education exhibited lower adaptive behavior compared with those with primary education. Although higher education is often expected to enhance adaptation through greater awareness and skills, evidence suggests that in many contexts, knowledge alone does not ensure action, especially when structural and contextual barriers are strong [64]. This finding is also consistent with the Theory of Planned Behavior, which posits that knowledge and positive attitudes alone do not necessarily lead to behavior unless individuals also possess sufficient perceived behavioral control and enabling conditions [65]. Studies on climate change adaptation in Bangladesh indicate that adaptation behavior is shaped not only by awareness and education levels, but also by access to resources, local livelihood needs, and contextual socioeconomic conditions [66]. Additionally, a systematic review of adaptation research in Bangladesh highlights that socioeconomic variables, including income, household capacity, and local decision‑making contexts, play a significant role in whether households implement adaptation strategies, often outweighing educational attainment [67]. Research also shows that climate knowledge and beliefs in Bangladesh are influenced by community exposure and access to information, suggesting that formal education may not be the strongest determinant of adaptation behavior in this context [68]. Overall, while education can raise awareness, actual climate adaptation is determined by the availability of resources, supportive institutions, and favorable social and community conditions that allow knowledge to be translated into action.
This study also explored a positive association between income and climate change adaptation. This finding aligns with the previous studies that emphasized that economic resources strongly influence adaptation, with households in low-resource settings having fewer options to respond to climate change [69–73]. Higher household income increases the financial capacity to invest in adaptive options such as home improvements, cooling, water management, and health-protective measures, which have been documented in household studies linking greater resources to increased adaptive actions [69]. In contrast, populations of lower socioeconomic status have fewer adaptation options to climate change [74]. In addition, households with lower incomes tend to depend on traditional or subsistence approaches and are less able to adopt innovative adaptation strategies [71]. Overall, these results suggest that household income affects both the ability to adapt and the long-term success of adaptation efforts, and that policies such as subsidies, microfinance, and targeted grants can help overcome financial barriers and encourage more effective adaptive actions.
Our finding that unemployed participants exhibited higher climate change knowledge and perception than employed participants may appear counterintuitive, given the common assumption that unemployment is associated with lower education and reduced access to information. However, unemployed individuals may have greater time and opportunity to engage with media, public messaging, and community outreach activities, potentially enhancing their awareness compared with those occupied with work commitments. This is supported by studies demonstrating that regular media exposure and information engagement are associated with higher climate-related awareness, including among unemployed populations in Bangladesh and similar settings (e.g., higher daily media use and unemployment were linked to greater climate–disease awareness in Dhaka) [75]. Additionally, community-based research in Bangladesh indicates that knowledge and perceptions of climate change are strongly influenced by exposure to information, education, and direct experience with climate-related events, rather than employment status alone [61]. However, the lack of statistical significance in our multivariate analysis suggests that this observed association may be confounded by other sociodemographic or contextual factors.
Our finding that individuals with cardiovascular disease (CVD) had higher knowledge but lower adaptation to climate change can be explained by the theoretical frameworks, such as the Health Belief Model (HBM). The HBM posits that health-related behavior is influenced by perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy [76]. In the context of CVD, patients are likely to have high perceived susceptibility and severity due to their physiological vulnerability, which increases their exposure to health advice and climate-related information, leading to greater knowledge. However, perceived barriers, including physical limitations that reduce mobility limit the capacity of CVD patients to translate knowledge into protective behaviors [77]. Consequently, CVD patients may be well informed but still unable to translate knowledge into effective adaptation. Beyond individual-level factors, contextual influences may also contribute. Cultural norms, environmental exposures, and socioeconomic constraints in Bangladesh, such as limited healthcare access, financial hardship, and inadequate housing, may further restrict adaptation, indicating that behavior is shaped not only by awareness but also by structural conditions [66]. These findings suggest that although CVD patients are well informed, they may face challenges in implementing effective adaptation strategies, highlighting the need for interventions that address both cognitive and contextual barriers. Future research should explore what cultural and social norms influence adaptation behaviors, how environmental conditions and climate hazard exposure affect adaptive capacity, how socioeconomic status and healthcare access shape the knowledge–adaptation relationship, and how interventions can be tailored to overcome physical and functional limitations among individuals with CVD.
This study also explored the positive correlation among climate change knowledge, attitude, and risk perception; these findings align with those of many previous studies across various countries [7–10,78]. These observations align with risk‑communication theory, which posits that better-informed individuals tend to perceive greater risk and develop more favorable attitudes toward mitigation, especially when they also believe in their ability to act (self-efficacy) [79]. Positive correlation between climate change knowledge and perception suggests that participants with the greater understanding of climate change perceived it as a more severe and imminent threat [80,81]. In addition, another study reported that holding positive beliefs about climate change makes people more likely to recognize it as a significant threat to themselves and society and to be concerned about its potential impacts [82]. Overall, the observed positive interrelationships among knowledge, attitude, and risk perception are consistent with existing research and underscore the importance of climate communication strategies that simultaneously enhance understanding, concern, and awareness of climate risks.
We also explored a negative correlation between knowledge of climate change and adaptation behavior. Knowledge is commonly viewed as an essential condition for the general public to take informed adaptation actions in response to climate change [9]. Although some research suggests that knowledge of climate change can directly motivate individuals to act in environmentally responsible ways [83–88], other studies highlight that knowledge by itself is often insufficient to produce actual behavioral change in the environmental domain [89–92]. However, effective adaptation depends on factors such as political will, resources, governance, and stakeholder cooperation, while barriers like poor coordination, limited public awareness, financial constraints, and reliance on external actors can hinder action in Bangladesh [93–96]. A study revealed that farmers of Bangladesh were also adopting poor adaptation strategies during crisis situations, such as migration [97]. Another study identified the high costs of agricultural intensification, labor shortages, insufficient irrigation or water for cultivation, and limited financial resources for investment as barriers to adaptation for farmers in Bangladesh [98]. In addition to local barriers, levels of affluence and economic development shape adaptation capacity, as wealthier contexts often have more resources and institutional support that enable effective action, whereas poorer contexts face financial and structural limits that constrain adaptation [99]. Moreover, access to resources and governance support is critical for translating climate knowledge into action, and its absence can weaken adaptation even when awareness is high [100]. Furthermore cultural values and social norms influence environmental responses, with variations such as individualism versus collectivism shaping how people perceive and respond to climate risks [101]. Lastly, this finding is also supported by integrated models of environmental behavior, which show that knowledge alone is insufficient, as behavior is more strongly shaped by social norms, motivation, and situational constraints [102,103]. Despite possessing knowledge about climate change, these barriers can restrict individuals’ ability to implement effective adaptation measures.
Overall, the study demonstrates that climate change knowledge, attitudes, and risk perception are interrelated and influence adaptation behaviours, but barriers can limit the translation of knowledge into practice. These findings have important implications for social sciences, highlighting the interaction of cognitive, behavioral, and contextual factors in shaping climate adaptation. They also inform policy by identifying priority areas for interventions that enhance awareness, reduce resource and infrastructural constraints, and support vulnerable populations, including those with chronic health conditions. By linking cognitive and affective factors to health-related adaptation, this study provides evidence to guide urban climate resilience strategies and evidence-based policy decisions aimed at protecting public health.
6. Strengths and limitations
A major strength of this study lies in its use of face-to-face interviews, which enabled interviewers to observe nonverbal cues, address respondent confusion, and enhance the accuracy and reliability of the collected data. The study offers important insights into health-related adaptation behaviors; however, it did not examine other forms of adaptation such as household energy-saving practices, disaster preparedness, or community-level actions, which may limit the breadth of the adaptation strategies assessed. Furthermore, the cross-sectional study design restricts causal interpretation, as all variables were measured at a single point in time. Future longitudinal research is needed to explore how knowledge, attitudes, and risk perceptions shape adaptation behaviors over time and to capture a more comprehensive range of adaptive responses.
7. Conclusions
This study examined climate change knowledge, attitudes, risk perception, and health-related adaptation behaviors among residents of the Dhaka Metropolitan Area. The results showed that although knowledge was positively associated with attitudes and risk perception, it was negatively linked with adaptation behaviors. These findings reflect broader contextual challenges common in developing urban settings, where limited resources and structural constraints often hinder the translation of awareness into action. In contrast, in more affluent contexts where higher income and institutional support may facilitate adaptive behaviors, economic vulnerability in cities like Dhaka can limit individuals’ capacity to respond effectively despite adequate knowledge. Moreover, collectivist social norms and community dependence, characteristic of many South Asian societies, may shape adaptation decisions differently than in more individualistic societies, emphasizing the role of shared practices and social expectations. To address the gap between knowledge and adaptive behavior, interventions should focus on reducing structural barriers and improving access to resources, including healthcare and climate-resilient infrastructure. Community-based approaches that leverage collectivist norms can enhance participation and shared responsibility. Additionally, behavior change strategies that strengthen perceived control and risk communication, supported by coordinated action from government and local institutions, are vital for fostering effective and equitable adaptation in Dhaka. Future research should investigate how structural constraints, socio-economic factors, and cultural norms influence the gap between climate knowledge and adaptation behavior, and evaluate the effectiveness of community-based, theory-driven interventions for promoting equitable adaptation in urban settings like Dhaka.
Acknowledgments
We would like to thank the people of Dhaka Metropolitan Area who participated voluntarily and gave their valuable time.
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