Next Article in Journal
Mechanical Performance and Shrinkage Behavior of Ultrahigh-Performance Concrete with Ferronickel Slag Under Various Curing Conditions
Previous Article in Journal
Low-Carbon Climate-Resilient Retrofit Pilot: Construction Report
Previous Article in Special Issue
Household Carbon Emissions Research from 2005 to 2024: An Analytical Review of Assessment, Influencing Factors, and Mitigation Pathways
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

From Building Emissions to Resident Well-Being: The Role of Environmental Pollution Perception

1
Chongqing Engineering Technology Research Center of Mine Soil Environmental Monitoring and Remediation (Chongqing Institute of Geology and Mineral Resources), Chongqing 401120, China
2
School of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
3
School of Engineering, Design & Built Environment, Western Sydney University, Locked Bag 1797, Kingswood, NSW 2751, Australia
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(20), 3669; https://doi.org/10.3390/buildings15203669 (registering DOI)
Submission received: 5 September 2025 / Revised: 6 October 2025 / Accepted: 9 October 2025 / Published: 12 October 2025

Abstract

In recent years, there has been growing recognition that reducing environmental pollution, particularly from building emissions, is essential for improving residents’ well-being. Buildings contribute substantially to worldwide greenhouse gas and pollutant emissions, making effective mitigation strategies a priority in achieving Sustainable Development Goals (SDGs). Using data from the 2021 China General Social Survey (CGSS), this study examines the relationship between perceived building environmental pollution and residents’ well-being, as well as the mechanism underlying this relationship, through an ordered probit model. The results indicate that higher levels of building environmental pollution significantly reduce residents’ well-being. To explore heterogeneity, the sample was further divided by urban–rural differences, local environmental protection expenditure level, and geographic region. The research found that residents with lower environmental protection expenditures, residents in rural areas and those in the central region are more likely to be negatively affected by building environmental pollution, with the correlation coefficients being −0.111, −0.104 and −0.101 respectively. Furthermore, the analysis indicates that annual income, the number of children, and type of work have moderating effects on this relationship, with correlation coefficients of 0.047, −0.054, and −0.095 respectively. Overall, this study provides empirical evidence for perceiving the social impact of building pollution in the context of building-related emissions and offers policy-related insights for strengthening environmental protection measures in the construction industry to enhance residents’ well-being.

1. Introduction

The rapid urbanization and expansion of the construction industry have driven a large consumption of fossil fuels, generating a significant amount of carbon emissions, leading to environmental pollution and having a negative impact on the quality of life of residents [1]. To address it, China attaches great importance to “ecological civilization”, a major social issue related to residents’ well-being [2]. According to the 2023 China Ecological and Environmental Conditions Bulletin, the environment issue has been focused on in terms of ambient air, surface water, seawater quality in jurisdictional waters, soil environmental risks, natural ecological conditions, and national urban acoustic environmental quality [3,4]. However, a considerable portion of pollution still originates from buildings, which involves various stages of construction, such as noise pollution [5], air pollution [6,7], heat island effect [8] and waste pollution [9]. The construction and building industry is a key source of carbon emissions in China, with a total carbon emission of 5.13 billion tons, accounting for 48.3% of the country’s energy-related carbon emissions. Specifically, carbon emissions during the operation stage of buildings amount to 2.31 billion tons, accounting for 21.7%, while carbon emissions during the construction process of the construction industry amount to 2.82 billion tons, accounting for 26.6%. In terms of carbon emissions from construction, house building and infrastructure construction contributed 1.84 billion tons and 980 million tons respectively. From a process perspective, the vast majority of carbon emissions originate from the production and transportation of building materials, reaching 2.72 billion tons, while direct emissions from the construction process amount to 100 million tons [10]. Reducing the carbon footprint of buildings has become an important consideration for sustainable development. Buildings can alleviate environmental problems to a certain extent [11,12,13]. Residents have a more direct perception of the environmental pollution caused by buildings. Therefore, exploring the relationship between building pollution and building residents’ well-being becomes crucial.
Existing studies on environment pollution show that different types of pollution can seriously harm the health and well-being of the population, which is a serious obstacle to the realization of the United Nations’ SDGs [14]. For example, long-term exposure to air pollution leads to cognitive decline [15] and long-term exposure to water pollution damages the nervous system [16]. Additionally, numerous studies have verified the impact of other environmental pollution on well-being, such as climate change [17,18] and noise pollution [19], all of these leading to a decrease in residents’ well-being.
Several studies have examined the relationship between building environmental issues and human psychological problems. For example, positive effects of good building conditions on their residents’ mental health have been proven [20]. However, China is facing the challenge of building pollution, and various types of environmental pollution may affect people’s psychological conditions and well-being [21]. The destruction of the building environment may have the risk of triggering the occurrence of depression and anxiety and the gradual development of eco-anxiety [22]. People may have a subjective belief that the environment will continue to be damaged and will impact their lives. These studies have demonstrated that the building environment not only has an impact on human physical health but also shows that there is an important psychological impact. Although the above studies contributed to exploring interactions between building pollution and community residents’ well-being, most of the studies have neglected the impact of subjective environmental feelings, and community residents’ subjective feelings about the deterioration of environmental problems may further affect their well-being [21].
In summary, against the global threat of building environmental problems, it has become imperative to consider how to take further measures to address environmental pollution and increase the well-being in both urban and rural areas. Based on the 2021 China General Social Survey (CGSS) data, which covers residents of various provinces in China, this study uses an ordered probit model to explore the role of residents’ perceived building environmental pollution on their well-being. Income, number of offspring, and type of work are used as moderating variables. The findings, to an extent, complement the existing research on related topics and provide specific references for coping with environmental pollution and increasing the well-being of residents, and at the same time, provide specific references to formulate strategies to increase the well-being of their community residents’ livelihoods.
The rest of the chapters are as follows: Section 2 is the theoretical framework and research hypotheses. Section 3 presents the constructed statistical model and describes the data sources and data processing. Section 4 conducts an analysis of building environmental perception and residents’ well-being, and describes the robustness test results and heterogeneity tests’ results. Section 5 summarizes the results of this study, provides policy recommendations, and finally describes the research limitations of this study.

2. Theoretical Framework and Research Hypotheses

2.1. Building Pollution Directly Affects the Well-Being of Residents

A building’s environment has an important impact on people [14]. However, it is worth noting that buildings generate a series of environmental pollution problems during the construction, operation and demolition stages. This includes noise pollution from construction equipment, construction dust pollution and pollution from discarded construction waste; just like other types of pollution, this will affect their living experience [1,5,7,8,9]. As users of buildings, residents may have a more direct perception of a series of pollutions produced by the building sector. However, the well-being of residents is not only affected by the objective building environment, but their subjective perception of the building environment may also affect their well-being. Concerns about environmental pollution have gradually developed into ecological anxiety, which has exacerbated concerns and anxieties [22]. The relevant media widely refer to ecological anxiety, and it is worth noting that society is in a period of rapid development, where residents face more uncertainty and significant stress, and the ecological anxiety felt varies depending on the social factors faced [23,24]. This perception is also influenced by many factors [25]. This suggests that residents may subjectively associate well-being with building environmental issues. Several studies have also explored the relationship between perceived environmental pollution and well-being. For example, based on the survey data, by modeling the structural equations of environmental risk, economic development and well-being, it was found that the perception of environmental risk hurts residents’ subjective well-being and socio-economic development [26]. Therefore, we speculate that the intensification of subjective environmental pollution will significantly reduce residents’ sense of happiness, and both subjective and objective pollution factors affect residents’ sense of happiness by influencing their physical and mental health. On this basis, hypothesis H1 is formulated.
H1. 
Higher perceptions of building environmental pollution lead to lower subjective well-being.

2.2. Mechanisms of the Impact of Perceived Building Environmental Pollution on Subjective Well-Being

Previous studies have found that income plays a significant role in people’s daily lives and influences their lifestyles. Different incomes lead to different lifestyles [27]. Some related studies have also confirmed this point. High-income families may increase their chances of obtaining a higher level of life experience. On the one hand, the higher the income, the more choices there are for consumption. They usually have a higher consumption level and also increase the frequency of leisure consumption. High income may be related to a higher level of happiness; the incomes of lower-income people can merely satisfy their basic personal needs, making it difficult for them to meet other higher level needs [28]. On the other hand, people also satisfy and achieve self-actualization by comparing income levels around them, and the higher the individual’s perceived socio-economic status, the more they can satisfy their needs in terms of self-esteem and self-actualization [29,30]. Faced with the impact of environmental problems, the effects of this additional stress from the income gap continue to intensify, and they may feel anxious or depressed [31,32]. The per capita disposable income between regions remains relatively large; from the beginning of 2012 until 2019, China’s per capita disposable income Gini coefficient remained within the range of 0.4–0.5, which reflects that the gap in the distribution of residents’ income is still at a significant level [33]. Therefore, we predict that people with higher income levels may have a higher sense of happiness. Based on this hypothesis, H2 is proposed.
H2. 
Faced with the problem of building environmental pollution, households with different incomes will face different pressures, which in turn will affect their sense of well-being.
Families may face pressures from various aspects of childcare [34,35]. On the one hand, a study has been conducted to investigate the relationship between childcare and parental stress in a sample of 13,390 parents in China, and the results show that most parents exhibit moderate levels of parenting stress and that the greater the number of children in the family, the greater the parental stress will be [36]. On the other hand, studies have shown that leisure time is positively correlated with well-being [37,38], but having children will result in less time for the individual [39]. Survey data from Australia and Germany examined how demographic events affect parents’ perceptions of stress. The results showed that couples experienced a significant increase in both time and financial stress after having a child [40]. For families with children, in addition to concerns about their health, the health of their children is also a concern and may increase stress in all areas. A more significant number of children may increase this stress [41]. More children may increase this kind of pressure, and the time they spend on their children may lead to a reduction in their personal leisure time. Based on this, hypothesis H3 is proposed.
H3. 
For families with a higher number of offspring, the additional stress associated with building-related environmental pollution further reduces residents’ well-being.
The construction of traditional rural residences has problems such as high energy consumption and serious pollution. During the construction, renovation and demolition of self-built houses in rural areas, construction waste is also produced. If not properly handled, it will have a certain impact on the agricultural ecology [42,43,44]. It is worth noting that agricultural activities are directly dependent on the natural environment, and environmental issues may have a certain impact on agricultural output [45,46]. A considerable number of self-built-house residents rely on agricultural income, so these pollutions may bring greater work pressure to agricultural workers. Based on data from structured interviews with farmers, it has been found that environmental changes threaten the standard of living of farmers and that livelihoods are the primary concern of farmers, focusing on the perceived impacts of environmental issues on production, income and health [47]. On the other hand, farmers in non-farm employment have higher life satisfaction; they may have higher incomes and more personal leisure time [48]. Environmental problems may reduce their income and affect their quality of life, which can decrease their well-being. Based on this, hypothesis H4 is proposed.
H4. 
The well-being of agricultural workers is more vulnerable to environmental pollution relative to non-agricultural workers.
Concerning the relevant studies mentioned above, this study further investigates the relationship between the perceived level of building environmental pollution and the influence on community residents’ well-being based on this study. Figure 1 illustrates the hypothesis of this study.

3. Methods and Data

3.1. Methodology

Considering that community residents’ subjective well-being is multinomial, this study’s explanatory variable has ordered and discrete characteristics. Therefore, concerning the excellent studies already conducted by scholars, this study adopts the ordered probability model, i.e., the probit model, to study the impact of community residents’ environmental awareness on their well-being and constructs the following benchmark regression model:
well-being i * = β 0 + β 1 E n v i r i + β 2 X i + ε i
well-being i = 1 well-being i * r 1 2 r 1 = well-being i * r 2 3 r 2 = well-being i * r 3 4 r 3 = well-being i * r 4 5 well-being i * r 5
In Equation (1) well-beingi* represents the subjective well-being of the ith resident, Enviri represents the perception of building pollution of the ith resident, Xi denotes a series of control variables representing different characteristics, β0 is used as a constant term, β1 and β2 are used as regression coefficients, and εi is a random perturbation term. Since the core explanatory variable well-beingi is categorized into five classes, r1–r5 in Equation (2) denotes the cut points to be estimated, respectively.

3.2. Data Sources and Interpretation

3.2.1. Data Sources

The data for this study come from the China General Social Survey Questionnaire (CGSS) published by the National Survey Research Center of Renmin University of China. CGSS is China’s earliest nationwide, comprehensive, and continuous academic survey project. CGSS is implemented by the China Survey and Data Center of Renmin University of China, and it has been conducted annually since 2003 as a continuous cross-sectional survey of more than 10,000 households in mainland China. We selected CGSS 2021 as the source of data for the study. CGSS 2021 is the latest research result of the project. It contains several modules on environment-related issues, which can better report the subjective attitudes of urban and rural residents towards the existing environmental problems in China. The raw data from CGSS 2021 contained 8148 households with survey data. The eligible samples were screened for this study; unknown and missing responses were excluded, and 1813 valid study samples were retained.

3.2.2. Interpretation of Variables

  • Dependent Variable
Well-being is a subjective and abstract concept, which, to a certain extent, reflects the community residents’ standard of living concerning the existing studies on the subjective well-being of the residents by scholars [26]. This study used responses to the question “Overall, what is your sense of well-being?” to measure community residents’ well-being(well-being). The responses to this question were generally effective in reflecting community residents’ well-being. This variable was used as an ordinal variable, and responses included “very unhappy,” “fairly unhappy,” “average,” “fairly happy,” or “very happy”. The responses are represented by values from 1 to 5, which indicate that well-being is increasing in ascending order.
2.
Independent Variable
Referring to the existing studies on perception of building pollution (Envir), community residents’ perceived level of environmental pollution can better reflect the current community residents’ views on environmental issues. Therefore, this study utilized the question “Do you think that the environmental problems that China is facing are serious?” to represent the community residents’ perceived level of building environmental pollution, and the responses include “Not serious at all”, ‘Not very serious’, ‘average’, ‘relatively serious’, or “extremely serious”; the responses are represented by values from 1 to 5, respectively, and indicate the increasing level of pollution perception.
3.
Moderator Variable
Annual income, number of children and type of job were selected as moderating variables in this study.
Annual income (Income) is an important factor in maintaining the everyday life of residents. The income level positively impacts well-being, and an increase in income often means an increase in the standard of living and material conditions of residents. We used the question “Your total annual income last year” as a measure of the annual income of the respondents, and based on the personal income of the questionnaire, we excluded the extreme samples and logarithmized the income variables.
The problem of environmental pollution may affect the development of the economy to some extent, and for agricultural workers, this will bring more uncertainty to their harvests. Therefore, we chose the type of work (Work) to measure the type of work that residents do. In the face of environmental problems, people with different types of jobs may have different subjective feelings, which affect their well-being to a different degree, so this study chose the question “What is your work status and experience?” to measure the type of work of the residents. Values 0–2 are used to reflect different types of work, namely “no work”, “non-agricultural work” and “agricultural work”.
Different family structures affect the well-being of family members. The number of children (Children) can affect the quality of life of the whole family to a certain extent, so we measure the total number of offspring based on the question “What is the number of your children (including stepchildren, adopted children, and deceased children)?”, and extreme sample values were excluded.
4.
Control Variable
In addition to the core explanatory variables, factors such as individual, social, and family characteristics also affect community residents’ well-being. Referring to similar studies, this study divides the control variables into individual, social, and family characteristics, respectively. Among them, personal characteristics include gender (Gender), age (Age), marital status (Marriage), ethnicity (Nation), health (Health) and education (Edu). Social characteristics include social security participation status (Insu), frequency of Internet use (Internet) and frequency of socialization (Interact). Family characteristics include family size (Famsize) and economic status (Famecom).
In addition, whether the quality of life is affected by the environment (Lifequli), urban–rural hukou (Hukou), and local environmental protection financial expenditures (EFE) are selected as the robustness test variables and the subgroup variables for studying urban–rural differences, respectively, where local environmental protection financial expenditures are used as the macro data, which are derived from the China Statistical Yearbook of the corresponding year.
Table 1 presents the descriptive statistical results of the variables in this study.

4. Empirical Evidence and Discussion

4.1. Effect of Perceived Level of Building Environmental Pollution on Well-Being

4.1.1. Benchmark Regression

In this study, the ordered probit model was used to estimate the effect of the perceived building environmental pollution on well-being, and the results are shown in Table 2. In order to ensure the robustness of the regression, Models (1)–(4) sequentially report the results of progressively incorporating control variables with different characteristics into the regression model. Model (1) demonstrates the regression results with the inclusion of only explanatory and interpreted variables, and the results show that the regression coefficients of Envir and well-being are negatively correlated at the 5% level. Model (2) demonstrates the regression results with the addition of control variables for personal characteristics, with the coefficients showing a negative correlation at the 5% level; Model (3) demonstrates the results with the addition of variables for social characteristics, with the regression coefficients showing a negative impact relationship at the 5% level, and Model (4) adds all the control variables into the regression model based on the previous model. The results show that the direction and significance of the influence of each variable remain relatively consistent with the previous model. This indicates that at the 5% level, an increase in the perceived level of building pollution will lead to a decline in happiness. This is similar to the research results of existing scholars [49]. Overall, residents with lower levels of perceived building environmental pollution have higher well-being.
Specifically, on the one hand, in modern society, residents will pay attention to the environment in which they work and live. Their subjective sense of well-being will be affected by the environment in which they live. A good environment plays an important role in improving residents’ quality of life, and a better living environment will, to a certain extent, improve residents’ subjective sense of well-being. On the contrary, a polluted environment may have a particular negative impact on residents’ lives [50]; this may expose them to the problem of “eco-anxiety”, where people feel nervous and anxious because of the uncertainty of the future environment [22]. On the other hand, well-being usually comes from activities that provide pleasure, jobs that provide fulfillment and income, and confidence in overcoming life’s obstacles, among others [51,52]. However, faced with the problem of environmental pollution, people have to expend extra energy to take a series of measures to prevent or address the effects of environmental pollution on them. This reduces their time for recreation and rest, reducing their sense of well-being. At the same time, higher perceived levels of environmental pollution may cause more stress in people’s lives. It has been shown that there is a negative correlation between residents’ stress and well-being and that people tend to be less happy when they feel more stress; those who are less stressed usually have higher well-being, and those who face more significant stress usually have a lower well-being [28]. As a result, residents with higher levels of perceived environmental pollution may feel more stress, reducing their well-being.
In Model (4), some control variables significantly correlated with residents’ well-being. An increase in Age, Health, Edu, Marriage, Interact, Internet, Famsieze, and Famecon will increase the residents’ well-being to some extent. This suggests that married people and those with high health, higher education, high socialization and use of the Internet, high family economic status and large family size usually have higher well-being, and women have higher well-being than men. Nation and Insu do not show a significant correlation with well-being.
Based on the above results and analysis, the increase in the degree of perceived building environmental pollution will significantly reduce happiness; hypothesis H1 is valid.

4.1.2. Endogeneity Test

The above results indicate that increased perceived building environmental pollution somewhat reduces residents’ well-being. However, endogeneity may lead to biased model estimation results. This study used the propensity score matching method, or PSM method, to investigate endogeneity. This method is applicable when the explanatory variables are 0–1 variables, so we redefine the explanatory variable, perceived building environmental pollution, from the original five levels, divide it into groups (those who answered with values of 1–2, those who answered with values of 3–5, those who think that there is an environmental problem (with the value of 1), and another group of those who do not think that there is an environmental problem (with the value of 0)), and then we respectively use nearest neighbor matching, radius matching and kernel matching for matching. Table 3 demonstrates the results of the endogeneity test of the impact of the perceived level of environmental pollution on residents’ well-being estimated by the PSM method, and the estimation results of different PSM sample matching methods are relatively consistent. The results of sample matching using the PSM method are consistent with the benchmark regression results mentioned above. This once again validates the robustness of the above results.

4.1.3. Robustness Tests

The robustness test of this study uses the replacement of explanatory variables and replacement regression. First, it has been shown that residents’ well-being is affected by their quality of life, and when residents’ quality of life is higher, it may promote their well-being [53,54]. Therefore, we selected the responses to the question “Do you think there is an association between the decline in the quality of life and the quality of the surrounding environment?” from the questionnaire to be used as a replacement variable for the explanatory variables, and we defined the responses as 0 = no association and 1 = association. The results of the robustness test are shown in Table 4. In Model (5), the perceived degree of environmental pollution is positively correlated with the quality of life at a significance level of 1%, with a correlation coefficient of 0.153. This indicates that when residents become aware of environmental pollution, they are more likely to associate the decline in their quality of life with environmental pollution issues. Secondly, we replaced the regression methods in the ordered probit model with the ordered Logit model and the OLS model respectively. Model (6) and Model (7) in Table 4 show the results of replacing the regression methods with the ordered Logit regression and the OLS regression, respectively, and their correlation coefficients are −0.096 and −0.035 respectively. The results of both regression methods show that at the 5% level, Envir is negatively correlated with well-being, which is relatively consistent with the results of the benchmark regression; that is, the perceived building environmental pollution will affect well-being. The above results indicate that the robustness test has passed, suggesting that our results are robust and further verifying that environmental pollution awareness can affect residents’ sense of happiness.
This verifies the robustness of hypothesis H1.

4.1.4. Heterogeneity Test

The above results have shown that an increase in the perception of building pollution will, to some extent, lead to a decrease in well-being. However, to investigate the impact variability among different groups further, the sample was divided into groups based on different characteristics. First, there are still differences in the living standards of urban and rural residents in China [55]. Problems such as urban and rural living environments, imbalances in the level of essential public services between urban and rural areas, and imbalances in the distribution of resources all lead to differences in their perceptions and subjective experiences of existing environmental problems, further contributing to differences in their sense of well-being. Second, increasing the scale of regional environmental protection financial expenditures may reduce residents’ pressure on environmental pollution prevention behaviors. Therefore, this study investigated the impact of the perceived level of environmental pollution on residents’ well-being based on the criteria of urban–rural hukou, geographic region differences and the level of regional environmental protection financial expenditures. Thirdly, China has a vast territory, and there is an imbalance in the development of regional economic levels. There is still an imbalance and uneven development in the eastern, central and western regions, and there are specific differences in the level of economic development and basic facilities in these regions. Although the standard of living in China’s central and western regions has improved, the level of economic development in the central and western regions generally still lags behind that in the eastern regions.
Models (8)–(10) in Table 5 show the results of the heterogeneity analysis for urban–rural differences, high and low financial expenditures on local environmental protection, and geographic location differences between the East and the West, respectively. Model (8) results indicate that the level of perceived environmental problems has a more significant impact on the well-being of rural residents and that an increase in the perceived level of environmental pollution will reduce the well-being of rural residents at the 1% significance level. However, this does not show the significance for urban residents. Existing environmental problems such as extreme weather, water pollution, and soil pollution will affect rural residents’ habitat and agricultural harvests, reducing the yield and final cost recovery of their agricultural products. The increase in environmental problems will lead to more significant agricultural production uncertainty. They may worry about the impact of environmental pollution on agricultural harvests, which will further lead to a decrease in their well-being.
In contrast, the perceived level of environmental problems is not the most important factor affecting the well-being of urban residents, probably because most urban residents do not have direct contact with the natural environment most of the time. Thus, they have a relatively weak perception of the direct harm caused by environmental change. At the same time, the public facilities in urban areas are more complete and abundant, and residents are more likely to obtain higher satisfaction with public services. Moreover, the higher level of service facilities can meet the residents’ expectations and satisfy their needs, which to a certain extent can “rebound” some of the negative impacts of the perceived level of environmental pollution on the sense of well-being [56,57].
Second, the average of local environmental protection financial expenditures in each region is derived based on statistical methods, and the average is used as the dividing line to divide the environmental protection financial expenditures into two groups: high and low. As shown in Model (9), the results indicate that the perceived level of environmental pollution significantly reduces residents’ well-being in areas with low financial expenditures on local environmental protection at the 1% level. This cost is invested in improving the environment and is used for financial expenditures, including environmental governance and guiding green development, and local governments may invest this expenditure in environmental governance [58]. Thus, with the same awareness of environmental pollution, residents perceive that this cost investment and the security of environmental governance increases their satisfaction with this aspect of environmental governance. They may feel more comfortable psychologically, which may mitigate to some extent the additional financial and energetic investment of the residents themselves in coping with the hazards of the natural environment, and thus does not significantly impact well-being [59,60]. Meanwhile, the results of the regional difference test show that environmental pollution awareness has a significant adverse effect on the well-being of residents in the central region at the 5% level, and the effect on the well-being of residents in the eastern and western regions is not significant.

4.2. Mechanism Testing

The results of the previous studies have shown that higher levels of perception of building pollution significantly reduce residents’ well-being, and this study further explored the moderating effects of perceived environmental pollution on well-being in terms of income, number of offspring, and type of work using a moderating effect model. The interaction terms between the perceived level of environmental pollution and income (Envir*income), number of children (Envir*child), and job type (Envir*Work) are first constructed in Model (11)–Model (13), respectively. Table 6 demonstrates the test results of the moderating effects.
First, the results of Model (11) show that the interaction term of income with the explanatory variables exhibits a positively correlated impact with the coefficient of the impact of the explanatory variables at the 5% level of significance. This indicates that higher household income mitigates the negative impact of increased perception of building pollution on residents’ well-being. As a result of the increase in income, there will be less pressure to spend on meeting daily needs, and they have more leisure consumption options and a higher level of consumption [28]; this can improve the standard of living to a certain extent and thus increase the sense of well-being. At the same time, average local incomes can be a reflection of the level of economic development, and incomes can also be considered a public service [61]. Higher levels of public services will increase residents’ life satisfaction to a certain extent, and residents’ access to higher quality public services will reduce their stress in coping with environmental problems, thus increasing their well-being. Therefore, hypothesis H2 is verified.
Secondly, from Model (12), it can be seen that the interaction term of the number of children with the explanatory variables shows a negative impact with the coefficient of the impact of the explanatory variables at the 1% level of significance. This suggests that a more significant number of offspring will contribute to the negative effect of increased perceived levels of environmental pollution on well-being. Stress has a significant indirect effect on all domains of quality of life [54]. In general, an increase in the number of children in a family affects the standard of living of the family as a whole, and parents and children living together will affect each other. If they have more children, they may face more pressure from their children, which may make their daily lives more financially and energetically demanding, and more pressure will reduce their well-being [28]. Therefore, hypothesis H3 is verified.
Finally, the results of Model (13) show that the interaction term between the type of work and the explanatory variables has a negatively correlated effect with the coefficient of the effect of the explanatory variables at the 1% level of significance. This suggests that agricultural workers are more concerned about the harm caused to them by building environmental pollution and that an increase in the perceived level of environmental pollution will have a greater impact on their well-being. Changes in the environment expose agricultural output to more significant uncertainty, which may have a direct impact on their financial resources. The positive correlation between well-being and productivity suggests that higher well-being also contributes to agricultural productivity. Therefore, hypothesis H4 is verified.

5. Conclusions

Based on CGSS 2021 household survey data, this study explores the influence of the perceived building environmental pollution on residents’ well-being. Heterogeneity in the degree of impact is explored based on urban–rural differences, the level of local financial expenditure on environmental protection, and geographic location differences. In addition, this study explores the moderating effects of average annual income, the number of children, and the type of job in order to understand better the mechanism of the influence of the degree of pollution perception on well-being. The results of the study are presented below:
First, an increase in the perceived level of building environmental pollution significantly reduces residents’ well-being. In addition to this, age, health, education and family size all have significant positive effects, and women’s well-being is slightly higher than men’s. Second, the results of the heterogeneity test show that an increase in the perceived level of environmental pollution has a significant adverse effect on the well-being of residents in rural areas, areas with low financial expenditures for local environmental protection, and central areas. Third, the results of the moderating effect mechanism test show that annual income, the number of children and the type of work all have a significant moderating effect and that higher household income can offset part of the negative impact of pollution perception on well-being. A higher number of offspring exacerbates the negative effect of perceived pollution on well-being. For agricultural workers, environmental pollution will bring more uncertainty to their agricultural activities, which also exacerbates the negative effect of perceived pollution on well-being.
To summarize, the results of this study can provide some reference for the government to deal with building environmental pollution problems and achieve the practical significance of increasing people’s well-being.
First of all, at the policy level, mandatory environmental protection regulations for construction should be formulated, establish a cross-departmental collaboration platform, and enhance regulatory efficiency. Meanwhile, a participation platform for technical support should be established. This includes developing a transparent and publicly accessible environmental data system. Through this platform, residents can monitor environmental indicators in real time and report issues such as illegal construction and dust, thereby directly reducing exposure risks and alleviating psychological anxiety through transparency. In addition, community-based incentives and capacity building are of vital importance. By promoting participatory environmental transformation and awarding “green Home” labels to families that carry out energy-saving renovations, residents’ sense of control and belonging can be enhanced.
Secondly, based on the differences between urban and rural areas, regional characteristics and the level of environmental investment, specific strategies need to be customized according to local conditions. For instance, in rural areas, policies should focus on providing subsidies to farmers and low-income families who adopt cost-effective green building technologies, thereby making sustainable options economically feasible. At the same time, different building environmental protection measures should be implemented according to different regions. In addition, to meet the specific needs of the population, targeted subsidies should be provided to multi-child families based on the number and age of children, to alleviate the economic pressure of creating a healthier living environment for the younger generation.
This study provides some references for exploring the path of influence between perceived building environmental pollution and community residents’ well-being; however, there are still some limitations. The survey data may be affected by the subjective emotions at the time of the survey, which may cause some indicators to deviate from the usual. Due to the availability of data, only one year of survey data was used, which makes it challenging to reflect the long-term impact of environmental awareness on well-being and the trend of change. Future studies should collect data from as many years as possible to discuss this long-term impact further. The precision of the study’s geographic variation needs to be improved; specific environmental conditions vary between regions, and future studies should improve geographic location precision.

Author Contributions

Conceptualization, Y.W. and P.R.; Supervision, Y.W.; Investigation, C.Z.; Project administration, Y.W.; Funding acquisition, P.R.; Software, Y.H.; Methodology, Y.H.; Writing—original Draft, Y.H.; Visualization, Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by General Project of Chongqing Natural Science Foundation (CSTB2025NSCQ-GPX0932), Chongqing Municipal Education Commission (Grant No. 23SKGH354).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank the anonymous reviewers for their valuable comments on this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
well-beingresidents’ well-being
Envirperceived building environmental pollution
Genderpersonal characteristics include gender
WorkType of Work
ChildrenThe number of children
IncomeAnnual income
AgeAge
MarriageMarriage Status
InsuSocial characteristics include social security participation status
EduEducation
NationEthnicity
FameconEconomic Status
FamsizeFamily Size
InternetFrequency of Internet use
InteractFrequency of Socialization
HealthHealth
EFELocal environmental protection financial expenditures
Lifequliwhether the quality of life is affected by the environment

References

  1. Liu, N.N.; Liu, R.; Huang, J.; Chen, L.S. Pollution, Happiness and Willingness to Pay Taxes: The Value Effect of Public Environmental Policies. Probl. Ekorozwoju 2018, 13, 75–86. [Google Scholar]
  2. Building a Beautiful China with Harmonious Coexistence Between Humans and Nature (Promoting High Quality Development Authoritative Release). Available online: http://paper.people.com.cn/rmrb/html/2024-09/26/nw.D110000renmrb_20240926_1-14.htm (accessed on 24 October 2024).
  3. 2023 Bulletin of China’s Ecological and Environmental Conditions. Available online: https://www.mee.gov.cn/hjzl/sthjzk/ (accessed on 24 October 2024).
  4. Ministry of Ecology and Environment Releases China’s Ecological Environment Status Bulletin 2023. Available online: https://www.gov.cn/lianbo/bumen/202406/content_6955727.htm (accessed on 15 October 2024).
  5. Yuan, M.; Yin, C.; Sun, Y.; Chen, W. Examining the associations between urban built environment and noise pollution in high-density high-rise urban areas: A case study in Wuhan, China. Sust. Cities Soc. 2019, 50, 101678. [Google Scholar] [CrossRef]
  6. Xin, S.; Li, Q.; Liu, S.; Ji, K. Dust dispersion patterns during building construction processes: A multi-process simulation study. J. Build. Eng. 2023, 77, 107478. [Google Scholar] [CrossRef]
  7. Dang, Z.; Qin, P. Research on Environmental Pollution Control of Fugitive Dust During Construction of Urban High-Rise Buildings. Fresenius Environ. Bull. 2022, 31, 525–532. [Google Scholar]
  8. Duan, S.; Luo, Z.; Yang, X.; Li, Y. The impact of building operations on urban heat/cool islands under urban densification: A comparison between naturally-ventilated and air-conditioned buildings. Appl. Energy 2019, 235, 129–138. [Google Scholar] [CrossRef]
  9. Su, S.; Li, S.; Ju, J.; Wang, Q.; Xu, Z. A building information modeling-based tool for estimating building demolition waste and evaluating its environmental impacts. Waste Manag. 2021, 134, 159–169. [Google Scholar] [CrossRef] [PubMed]
  10. 2024 China Urban and Rural Construction Field Carbon Emission Research Report. Available online: https://cn.chinadaily.com.cn/a/202501/20/WS678e178ba310be53ce3f2940.html (accessed on 21 September 2025).
  11. Tigani, D.; van Kan, D.; Tennakoon, G.; Geng, L.; Chan, M. Measuring Embodied Carbon of Buildings: A Review of Methodologies and Benchmarking Towards Net Zero. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Wuhan, China, 31 May–1 June 2024; p. 012030. [Google Scholar]
  12. Dang, P.; Geng, L.; Niu, Z.; Chan, M.; Yang, W.; Gao, S. A value-based network analysis for stakeholder engagement through prefabricated construction life cycle: Evidence from China. J. Civ. Eng. Manag. 2024, 30, 49–66. [Google Scholar] [CrossRef]
  13. Wang, B.; Geng, L.N.; Tam, V.W.Y. Effective carbon responsibility allocation in construction supply chain under the carbon trading policy. Energy 2025, 319, 135059. [Google Scholar] [CrossRef]
  14. Li, C.G.; Fan, X.B.; Wang, Y.T.; Wang, Z.G.; Dang, Y.X.; Cui, Y.Z. Can the development of renewable energy in China compensate for the damage caused by environmental pollution to residents’ health? Environ. Sci. Pollut. Res. 2023, 30, 92636–92650. [Google Scholar] [CrossRef]
  15. Brabhukumr, A.; Malhi, P.; Ravindra, K.; Lakshmi, P.V.M. Exposure to household air pollution during first 3 years of life and IQ level among 6-8-year-old children in India—A cross-sectional study. Sci. Total Environ. 2020, 709, 135110. [Google Scholar] [CrossRef] [PubMed]
  16. Huang, Y.; Dai, Y.; Li, M.; Guo, L.; Cao, C.; Huang, Y.; Ma, R.; Qiu, S.; Su, X.; Zhong, K. Exposure to cadmium induces neuroinflammation and impairs ciliogenesis in hESC-derived 3D cerebral organoids. Sci. Total Environ. 2021, 797, 149043. [Google Scholar] [CrossRef]
  17. Zapata, O. Happiness in the tropics: Climate variables and subjective wellbeing. Environ. Dev. Econ. 2022, 27, 250–271. [Google Scholar] [CrossRef]
  18. Rehdanz, K.; Maddison, D. Climate and happiness. Ecol. Econ. 2005, 52, 111–125. [Google Scholar] [CrossRef]
  19. Su, L.L.; Zhou, S.H.; Kwan, M.P.; Chai, Y.W.; Zhang, X. The impact of immediate urban environments on people’s momentary happiness. Urban. Stud. 2022, 59, 140–160. [Google Scholar] [CrossRef]
  20. Zhu, D.F.; Zhong, L.S.; Yu, H. Progress on Relationship between Natural Environment and Mental Health in China. Sustainability 2021, 13, 991. [Google Scholar] [CrossRef]
  21. Li, Y.; Guan, D.; Tao, S.; Wang, X.; He, K. A Review of Air Pollution Impact on Subjective Well-being: Survey versus Visual Psychophysics. J. Clean. Prod. 2018, 184, 959–968. [Google Scholar] [CrossRef]
  22. Hickman, C.; Marks, E.; Pihkala, P.; Clayton, S.; Lewandowski, R.E.; Mayall, E.E.; Wray, B.; Mellor, C.; Van Susteren, L. Climate anxiety in children and young people and their beliefs about government responses to climate change: A global survey. Lancet Planet. Health 2021, 5, e863–e873. [Google Scholar] [CrossRef]
  23. Pihkala, P. Anxiety and the Ecological Crisis: An Analysis of Eco-Anxiety and Climate Anxiety. Sustainability 2020, 12, 7836. [Google Scholar] [CrossRef]
  24. Slovic, P. Perception of risk. In The Perception of Risk; Routledge: Abingdon, UK, 2016; pp. 220–231. [Google Scholar]
  25. Uzzell, D.L. The psycho-spatial dimension of global environmental problems. J. Environ. Psychol. 2000, 20, 307–318. [Google Scholar] [CrossRef]
  26. Xu, G.; Feng, X.N.; Li, Y.W.; Chen, X.H.; Jia, J.M. Environmental risk perception and its influence on well-being. Chin. Manag. Stud. 2017, 11, 35–50. [Google Scholar] [CrossRef]
  27. Diener, E.; Oishi, S.; Tay, L. Advances in subjective well-being research. Nat. Hum. Behav. 2018, 2, 253–260. [Google Scholar] [CrossRef]
  28. Demenech, L.M.; Almeida, R.B.; Neiva-Silva, L.; Dumith, S.C. Does money buy happiness? Disentangling the association between income, happiness and stress. An. Acad. Bras. Ciências 2022, 94, e20210364. [Google Scholar] [CrossRef]
  29. Xu, S.; Chen, P. Income, social status, and happiness: Fairness perception perspective. J. Manag. Sci. China 2017, 20, 99–116. [Google Scholar]
  30. Huang, T.; Liu, L.; Wang, D.; Zhang, W. Socioeconomic status and sociometric status: Age differences on the effects of social comparison on subjective well-being. Acta Psychol. Sin. 2016, 48, 1163. [Google Scholar] [CrossRef]
  31. Cristóbal-Narváez, P.; Haro, J.M.; Koyanagi, A. Perceived stress and depression in 45 low- and middle-income countries. J. Affect. Disord. 2020, 274, 799–805. [Google Scholar] [CrossRef]
  32. Patel, V.; Burns, J.K.; Dhingra, M.; Tarver, L.; Kohrt, B.A.; Lund, C. Income inequality and depression: A systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry 2018, 17, 76–89. [Google Scholar] [CrossRef] [PubMed]
  33. What Is the Gini Coefficient. Available online: https://www.stats.gov.cn/zs/tjws/tjzb/202301/t20230101_1903941.html (accessed on 9 November 2024).
  34. Cooper, C.E.; McLanahan, S.S.; Meadows, S.O.; Brooks-Gunn, J. Family Structure Transitions and Maternal Parenting Stress. J. Marriage Fam. 2009, 71, 558–574. [Google Scholar] [CrossRef] [PubMed]
  35. Nomaguchi, K.; Milkie, M.A. Sociological perspectives on parenting stress: How social structure and culture shape parental strain and the well-being of parents and children. In Parental Stress and Early Child Development: Adaptive and Maladaptive Outcomes; Springer: Berlin/Heidelberg, Germany, 2017; pp. 47–73. [Google Scholar]
  36. Hong, X.; Zhu, W.; Zhao, S. Type of family support for infant and toddler care that relieves parenting stress: Does the number of children matter? Healthcare 2023, 11, 421. [Google Scholar] [CrossRef] [PubMed]
  37. Wei, X.; Huang, S.; Stodolska, M.; Yu, Y.H. Leisure Time, Leisure Activities, and Happiness in China: Evidence from a National Survey. J. Leis. Res. 2015, 47, 556–576. [Google Scholar] [CrossRef]
  38. Michèle, J.; Guillaume, M.; Alain, T.; Nathalie, B.; Claude, F.; Kamel, G. Social and leisure activity profiles and well-being among the older adults: A longitudinal study. Aging Ment. Health 2019, 23, 77–83. [Google Scholar] [CrossRef]
  39. Bradbury, B. Time and the Cost of Children. Rev. Income Wealth 2008, 54, 305–323. [Google Scholar] [CrossRef]
  40. Buddelmeyer, H.; Hamermesh, D.S.; Wooden, M. THE stress cost of children on moms and dads. Eur. Econ. Rev. 2018, 109, 148–161. [Google Scholar] [CrossRef]
  41. Hong, X.M.; Liu, Q.Q. Parenting stress, social support and parenting self-efficacy in Chinese families: Does the number of children matter? Early Child. Dev. Care 2021, 191, 2269–2280. [Google Scholar] [CrossRef]
  42. Liao, C.; Zhao, D.; Zhang, S.; Chen, L. Determinants and the moderating effect of perceived policy effectiveness on residents’ separation intention for rural household solid waste. Int. J. Environ. Res. Public. Health 2018, 15, 726. [Google Scholar] [CrossRef]
  43. Qiu, X.; Zou, B. Determinants of household waste separation in rural China. J. Mater. Cycles Waste Manag. 2024, 26, 3446–3459. [Google Scholar] [CrossRef]
  44. Bai, L.; Wang, H.; Shi, C.; Du, Q.; Li, Y. Assessment of SIP buildings for sustainable development in rural China using AHP-grey correlation analysis. Int. J. Environ. Res. Public. Health 2017, 14, 1292. [Google Scholar] [CrossRef]
  45. Maia, A.G.; Miyamoto, B.C.B.; Garcia, R. Climate Change and Agriculture: Do Environmental Preservation and Ecosystem Services Matter? Ecol. Econ. 2018, 152, 27–39. [Google Scholar] [CrossRef]
  46. Agovino, M.; Casaccia, M.; Ciommi, M.; Ferrara, M.; Marchesano, K. Agriculture, climate change and sustainability: The case of EU-28. Ecol. Indic. 2019, 105, 525–543. [Google Scholar] [CrossRef]
  47. Dang, H.L.; Li, E.; Nuberg, I.; Bruwer, J. Farmers’ Perceived Risks of Climate Change and Influencing Factors: A Study in the Mekong Delta, Vietnam. Environ. Manag. 2014, 54, 331–345. [Google Scholar] [CrossRef]
  48. Liu, Y.; Chen, M.J.; Yu, J.Y.; Wang, X.B. Being a happy farmer: Technology adoption and subjective well-being. J. Econ. Behav. Organ. 2024, 221, 385–405. [Google Scholar] [CrossRef]
  49. Li, F.; Zhou, T. Effects of objective and subjective environmental pollution on well-being in urban China: A structural equation model approach. Soc. Sci. Med. 2020, 249, 112859. [Google Scholar] [CrossRef]
  50. Chen, J.; Hassan, T.; Zhao, D. Does awareness of environmental pollution increase electricity consumption? A view from household survey of China. Environ. Sci. Pollut. Res. 2023, 30, 13532–13550. [Google Scholar] [CrossRef] [PubMed]
  51. You, S.; Neoh, K.G.; Tong, Y.W.; Dai, Y.J.; Wang, C.H. Variation of household electricity consumption and potential impact of outdoor PM2.5 concentration: A comparison between Singapore and Shanghai. Appl. Energy 2017, 188, 475–484. [Google Scholar] [CrossRef]
  52. Dolan, P.; Peasgood, T.; White, M. Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. J. Econ. Psychol. 2008, 29, 94–122. [Google Scholar] [CrossRef]
  53. Huete-Alcocer, N.; Lopez-Ruiz, V.R.; Alfaro-Navarro, J.L.; Nevado-Pena, D. European Citizens’ Happiness: Key Factors and the Mediating Effect of Quality of Life, a PLS Approach. Mathematics 2022, 10, 367. [Google Scholar] [CrossRef]
  54. Aytaç, S.; Bayram-Arli, N.; Dev, M.A. Stress and Quality of Life: The Mediating Role of Happiness. J. Econ. Cult. Soc. 2024, 69, 46–55. [Google Scholar] [CrossRef]
  55. Tian, Z.; Tian, Y.; Shen, L.; Shao, S. The health effect of household cooking fuel choice in China: An urban-rural gap perspective. Technol. Forecast. Soc. Change 2021, 173, 121083. [Google Scholar] [CrossRef]
  56. Frederiks, E.R.; Stenner, K.; Hobman, E.V. The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review. Energies 2015, 8, 573–609. [Google Scholar] [CrossRef]
  57. Hu, S.J.; Zeng, R.X.; Yi, C.Z. Media Use and Environmental Public Service Satisfaction-An Empirical Analysis Based on China. Sustainability 2019, 11, 3873. [Google Scholar] [CrossRef]
  58. Cheng, S.L.; Wang, K.X.; Meng, F.X.; Liu, G.Y.; An, J.F. The unanticipated role of fiscal environmental expenditure in accelerating household carbon emissions: Evidence from China. Energy Policy 2024, 185, 113962. [Google Scholar] [CrossRef]
  59. Li, M.; Zhao, P.; Wu, L.; Chen, K. Effects of Value Perception, Environmental Regulation and Their Interaction on the Improvement of Herdsmen’s Grassland Ecological Policy Satisfaction. Int. J. Environ. Res. Public. Health 2021, 18, 3078. [Google Scholar] [CrossRef] [PubMed]
  60. Wang, H.S.; Yang, G.Q.; Ouyang, X.; Tan, Z.Y. Does environmental information disclosure promote the supply of environmental public goods? Evidence based on a dynamic spatial panel Durbin model. Environ. Impact Assess. Rev. 2022, 93, 106725. [Google Scholar] [CrossRef]
  61. Lee, C.-C.; Xing, W.; Lee, C.-C. The impact of energy security on income inequality: The key role of economic development. Energy 2022, 248, 123564. [Google Scholar] [CrossRef]
Figure 1. Research hypothesis.
Figure 1. Research hypothesis.
Buildings 15 03669 g001
Table 1. Descriptive statistical results.
Table 1. Descriptive statistical results.
VariableMeanSDMinMaxN
well-being4.0100.800151813
Envir3.0201.120151813
Gender0.5100.500011813
Work0.7600.710021813
Children1.5901.130071813
Income10.131.3305.70013.591813
Age52.4816.5618941813
Marriage0.7200.450011813
Insu0.7800.420011813
Edu2.3401.110141813
Nation1.0700.250121813
Famecon2.6100.800151813
Famsize3.3101.8501151813
Internet2.3701.780151813
Interact2.6401.100151813
Health3.5401.050151813
EFE203.77047.71351.61813
Lifequli0.1700.380011813
Table 2. Benchmark regression results.
Table 2. Benchmark regression results.
Well-Being
Model (1)Model (2)Model (3)Model (4)
Envir−0.061 **−0.064 **−0.063 **−0.055 **
(0.024)(0.025)(0.025)(0.025)
Gender −0.090 *−0.089−0.101 *
(0.054)(0.054)(0.054)
Age 0.014 ***0.013 ***0.011 ***
(0.002)(0.002)(0.002)
Health 0.261 ***0.260 ***0.228 ***
(0.031)(0.031)(0.032)
Nation 0.0990.1240.131
(0.131)(0.129)(0.134)
Edu 0.178 ***0.186 ***0.133 ***
(0.031)(0.032)(0.034)
Marriage 0.153 **0.165 ***0.121 *
(0.060)(0.061)(0.063)
Insu −0.006−0.030
(0.068)(0.069)
Interact 0.066 **0.060 **
(0.026)(0.026)
Internet 0.0290.038 *
(0.021)(0.022)
Famsize 0.055 ***
(0.016)
Famecon 0.277 ***
(0.041)
Province fixedYesYesYesYes
N1813181318131813
Note: *, ** and *** show the significance levels of 10%, 5% and 1% respectively.
Table 3. Endogeneity test results.
Table 3. Endogeneity test results.
MethodsTypesTreated ValueControl ValueDifferenceS.ET-Stat
unmatchedBefore3.9554.072−0.1170.038−3.12 ***
neighbor matchingAfter3.9554.039−0.0840.051−1.65 *
radius matchingAfter3.9554.054−0.0990.042−2.39 **
kernel matchingAfter3.9554.055−0.1000.042−2.40 **
Note: *, ** and *** show the significance levels of 10%, 5% and 1% respectively.
Table 4. Robustness test results.
Table 4. Robustness test results.
LifequliWell-BeingWell-Being
Model (5)Model (6)Model (7)
Envir0.153 ***−0.096 **−0.035 **
(0.033)(0.046)(0.017)
Gender0.003−0.206 **−0.070 *
(0.073)(0.096)(0.036)
Age−0.010 ***0.019 ***0.007 ***
(0.003)(0.004)(0.002)
Health−0.111 ***0.417 ***0.151 ***
(0.038)(0.060)(0.022)
Nation0.431 ***0.2540.072
(0.158)(0.247)(0.088)
Edu−0.0760.210 ***0.087 ***
(0.048)(0.060)(0.023)
Marriage0.0440.195 *0.094 **
(0.087)(0.114)(0.043)
Insu0.093−0.042−0.020
(0.092)(0.124)(0.046)
Interact0.0120.098 **0.040 **
(0.034)(0.047)(0.017)
Internet0.0210.072 *0.022
(0.027)(0.038)(0.014)
Famsize0.0100.100 ***0.032 ***
(0.021)(0.028)(0.010)
Famecon−0.168 ***0.507 ***0.185 ***
(0.051)(0.074)(0.028)
Cons 2.549 ***
(0.228)
Province fixedYesYesYes
N181318131813
adj. R2 0.134
Note: *, ** and *** show the significance levels of 10%, 5% and 1% respectively.
Table 5. Heterogeneity test results.
Table 5. Heterogeneity test results.
Model (8)Model (9)Model (10)
RuralUrbanHighLowEastCentralWest
Envir−0.104 ***0.0250.014−0.111 ***−0.022−0.101 **−0.051
(0.034)(0.038)(0.037)(0.035)(0.039)(0.045)(0.051)
ControlsYESYESYESYESYESYESYES
Province fixedYESYESYESYESYESYESYES
Observations982831878935786554473
Note: ** and *** show the significance levels of 5% and 1% respectively.
Table 6. Mechanism test results.
Table 6. Mechanism test results.
Model (11)Model (12)Model (13)
IncomeChildrenWork
Envir−0.531 ***0.0340.023
(0.190)(0.041)(0.038)
Income−0.168 ***
(0.061)
Envir*income0.047 **
(0.019)
Children 0.263 ***
(0.069)
Envir*child −0.054 ***
(0.021)
Work 0.272 **
(0.106)
Envir*Work −0.095 ***
(0.033)
ControlsYESYESYES
Province fixedYESYESYES
N181318131813
Note: ** and *** show the significance levels of 5% and 1% respectively.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; He, Y.; Zheng, C.; Rahnamayiezekavat, P. From Building Emissions to Resident Well-Being: The Role of Environmental Pollution Perception. Buildings 2025, 15, 3669. https://doi.org/10.3390/buildings15203669

AMA Style

Wang Y, He Y, Zheng C, Rahnamayiezekavat P. From Building Emissions to Resident Well-Being: The Role of Environmental Pollution Perception. Buildings. 2025; 15(20):3669. https://doi.org/10.3390/buildings15203669

Chicago/Turabian Style

Wang, Yuanping, Yu He, Caigui Zheng, and Payam Rahnamayiezekavat. 2025. "From Building Emissions to Resident Well-Being: The Role of Environmental Pollution Perception" Buildings 15, no. 20: 3669. https://doi.org/10.3390/buildings15203669

APA Style

Wang, Y., He, Y., Zheng, C., & Rahnamayiezekavat, P. (2025). From Building Emissions to Resident Well-Being: The Role of Environmental Pollution Perception. Buildings, 15(20), 3669. https://doi.org/10.3390/buildings15203669

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop