Sustainable Consumption and Residents’ Happiness: An Empirical Analysis Based on the 2021 Chinese General Social Survey (CGSS2021)

Abstract

Sustainable consumption is a fundamental driver for implementing sustainable development strategies and is crucial in advancing Chinese-style modernization. Utilizing data from the Chinese General Social Survey 2021 (CGSS2021), this study examines the relationship between sustainable consumption and residents’ happiness, classifying its effects into growth and sustainability. The study results show that (1) sustainable consumption positively influences residents’ happiness. (2) Regionally, in the central and western regions, sustainable consumption significantly enhances residents’ happiness, with the sustainable effect surpassing the growth effect. In contrast, in the eastern region, sustainable consumption alone does not substantially improve residents’ happiness; instead, the growth effect predominates. (3) From an urban–rural perspective, sustainable consumption notably impacts happiness in rural areas. In contrast, urban areas primarily benefit from the growth effect of sustainable consumption, with the sustainable effect being less significant. (4) From an age-related perspective, different factors predominantly influence the happiness of various age groups. For young people, health plays a crucial role in their overall happiness. In contrast, middle-aged groups place more importance on their marital status, while older people are primarily concerned with housing conditions. (5) Income analysis shows that income increases substantially impact the happiness of high-income groups compared to low-income groups. Based on these insights, we recommend enhancing education and guidance on sustainable consumption, implementing regionally differentiated policies, increasing support for green consumption in rural areas, developing age-specific policies, and addressing income disparities. These measures can enhance the residents’ happiness more effectively and contribute to sustainable societal development.

1. Introduction

Against sustained global economic growth, escalating pressures on resources and the ecological environment present a significant challenge to advancing Chinese-style modernization. Although the traditional economic growth model has substantially improved living standards, it has also contributed to substantial issues such as resource depletion, environmental pollution, and ecological imbalance. This highlights the drawbacks of prioritizing economic growth at the expense of environmental protection and sustainable resource utilization. Consequently, sustainable consumption, which integrates economic, social, and environmental benefits, has become crucial in advancing Chinese-style modernization. This approach addresses the development needs of the present generation and ensures the conservation of resources and the ecological environment for future generations. Moreover, sustainable consumption alleviates current pressures on resources and the environment while guiding residents toward healthier and more efficient lifestyles. Sustainable consumption is increasingly prominent in policy formulation and practice promotion. Its significance relates closely to individual and social happiness and the long-term sustainable development of societies.

Meanwhile, research into the factors influencing residents’ happiness and related empirical studies has gained prominence. However, systematic and in-depth investigations into the relationship between sustainable consumption and residents’ happiness remain limited, hindering a comprehensive understanding of how sustainable consumption impacts happiness and its mechanisms across various regional and urban–rural contexts. To address this gap, this study uses data from the Chinese General Social Survey 2021 (CGSS2021) to systematically examine the impact of sustainable consumption on residents’ happiness. This study raises several vital questions: Can sustainable consumption further improve residents’ happiness in the context of ongoing economic development? How do significant disparities in China—between regions, between urban and rural areas, and between the wealthy and the poor—affect the influence of sustainable consumption on happiness? How do these disparities influence the impact of sustainable consumption on residents’ happiness? If differences are present, what mechanisms drive these varying effects? Additionally, how do different age groups respond to sustainable consumption? Finally, how do income groups differ in their happiness about sustainable consumption? We classify the impacts of sustainable consumption into growth and sustainability effects, analyze its mechanisms on residents’ happiness in-depth, and investigate how its effects vary across different regional and urban–rural contexts. This approach deepens the understanding of the relationship between sustainable consumption and subjective happiness and lays the groundwork for more targeted policies to promote societal sustainable development. This study follows this structure: Section 2 reviews the relevant literature; Section 3 examines the mechanisms by which sustainable consumption impacts happiness; Section 4 outlines the research methodology and data sources; Section 5 presents the empirical analysis results and discussion; and Section 6 summarizes the research findings and offers policy recommendations.

2. Literature Review

2.1. Studies on the Concept and Interpretation of Sustainable Consumption

The term “sustainable consumption” was formally introduced at the World Summit on Sustainable Development (WSSD) in 1992 in response to the growing sustainability challenges confronting humanity [1]. Within academic circles, sustainable consumption is frequently defined as “the use of services and related products to meet basic needs and improve quality of life while minimizing the use of natural resources and toxic materials, and reducing waste and pollutants throughout the product life cycle, to avoid compromising the needs of future generations” [2,3,4,5]. According to Haider et al. [6], sustainable consumption embodies a paradoxical relationship between two inherently opposing concepts: “sustainable” and “consumption”. While “sustainable” emphasizes the protection of the ecological environment and the conservation of resources, “consumption” often entails environmental degradation and resource wastage. Shao [7] further elaborates that sustainable consumption involves adhering to principles of reuse and minimization. It encourages consumers to prioritize sustainable products, emphasizing how they are reused or disposed of and advocating for social and environmental causes. Sharma et al. [8] argue that sustainable consumption seeks to enhance the quality of life by utilizing fewer resources and fostering lifestyles aligned with sustainable development. Sustainable consumption has sparked debate as an umbrella term, with its definition remaining contested and lacking consensus [9]. This ongoing debate implies that the concept is multifaceted and comprehensive, often shaped by the specific objectives of scholarly research [10].

Therefore, in line with our research objectives and the phenomena under investigation, we interpret sustainable consumption as follows: First, it requires the embodiment of ecological balance, emphasizing harmony between consumption behaviors and the natural environment. Second, it prioritizes intergenerational equity, aiming to meet the needs of the present generation without compromising the ability of future generations to fulfill their own needs. Third, sustainable consumption underscores the evolving nature of consumption patterns, which should adapt to economic development. As economic development progresses, we expect the scope and quality of consumption to evolve, transitioning from a primary focus on material consumption to an increased pursuit of spiritual and experiential consumption. Finally, sustainable consumption aims to enhance the quality of life, not by merely restricting consumption to protect the ecological environment but by striving to achieve a harmonious balance between consumption and environmental conservation. In summary, sustainable consumption is a behavioral process that considers the survival and development of current and future generations within the ecological and natural resource constraints. It aims to enhance the quality of life, promote the efficient development and rational utilization of natural resources, and minimize negative impacts on the ecological environment.

2.2. Research on the Relationship between Sustainable Consumption and Happiness

The relationship between sustainable consumption and happiness is complex and multidimensional. Happiness can be a prerequisite for sustainable consumption, while sustainable consumption can also drive happiness, indicating interrelationships between these concepts [11]. To achieve sustainable consumption, it is essential to understand the root causes of current unsustainable patterns, encompassing both systemic and individual factors [12]. This understanding paves the way for promoting sustainable consumption as a means to enhance happiness. Furthermore, Chéron et al. [13] demonstrated through data analysis that sustainable shopping positively impacts life satisfaction and overall happiness, reinforcing the intricate relationship between sustainable purchasing choices and happiness. Carrero et al. [14] highlighted that the relationship between sustainable consumption and happiness is more complex than previous studies suggest, finding that happiness primarily arises from simplifying behaviors. In contrast, positive consumption behaviors may be associated with lower levels of mental health. Meanwhile, other scholars have analyzed happiness regarding its response to consumption, proposing that higher happiness levels generally correlate with increased consumption expenditures. Higher happiness levels are significantly linked to greater spending on basic necessities, education, and gifts [15]. Issock et al. [16] propose that organic food consumption plays a central role in providing consumers with pleasure, positive emotions, fulfillment, and personal growth, emphasizing that health consciousness significantly impacts happiness. Ramos-Hidalgo et al. [17] also demonstrate a positive correlation between consumers’ propensity for sustainable behaviors and their happiness. In other words, consumers enhance their happiness by engaging in sustainable practices. Sameer et al. [18] point out a correlation between happiness and higher consumption levels, which may motivate individuals to adopt more responsible behaviors and intensify their focus on sustainability issues. Whether sustainable consumption acts as an independent variable affecting happiness or happiness itself serves as an independent variable, the relationship between sustainable consumption and happiness is dynamic and complex. This study primarily focuses on analyzing the impact of sustainable consumption on residents’ happiness.

To this end, based on the review of existing studies, we propose the following hypotheses:

H1: 

Sustainable consumption generally positively impacts residents’ happiness.

Specifically, sustainable consumption influences residents’ happiness through growth and sustainability effects. Exploring how sustainable consumption impacts residents’ happiness will enhance our understanding of its effects and mechanisms.

2.3. Research on the Relationship between Regional Differences and Happiness

Additionally, the urban–rural gap influences happiness; Feng and Chen [19] demonstrated that reducing this gap improves the happiness of rural residents but negatively affects urban residents’ happiness. Zhang and Wan [20] examined regional differences in the happiness of rural residents in China, suggesting that farmers in the east are significantly happier than those in the central and western regions. Elburz et al. [21] argued that urban residents tend to be happier, noting that city size is crucial in enhancing residents’ happiness, while city density negatively impacts it. Nasser and Fakhroo [22] identified regional differences in happiness levels among cities and emphasized the need for a multifaceted approach to address disparities, mainly targeting disadvantaged groups. Zhu et al. argue that the happiness of rural households significantly influences their consumption expenditures compared to urban households. Therefore, enhancing the happiness of rural residents by increasing their income and investing in rural digital technology infrastructure is crucial for improving their sense of happiness. Additionally, regional differences and perceptions of urban–rural equity also affect the happiness of rural residents [23]. Zhang et al. [24] suggest that in general, residents in the west maintain a high level of happiness, with urban residents reporting a mean happiness value slightly higher than that of rural residents. A more recent study highlights the global diversity of the urban–rural happiness gap, suggesting that the size and direction of this gap can be subtle and may vary according to individual and geographic factors. This study reveals a causal relationship between the urban–rural continuum and multidimensional happiness [25], guiding promoting happiness in rural and urban areas through policy development and practice. Furthermore, differences in life satisfaction between urban and rural residents vary with the level of development [26]. In conclusion, whether approached from a cross-sectional or longitudinal perspective, regional differences in happiness persist, indicating a continued need for further research.

To this end, based on the review of existing studies, we propose the following hypotheses:

H2: 

Regional differences significantly affect the enhancement of residents’ happiness through sustainable consumption.

Specifically, the effect of sustainable consumption is more significant in the central and western regions than in the eastern regions. The happiness of rural residents is primarily enhanced through the sustainability effect, while the growth effect more influences the happiness of urban residents. This hypothesis aims to reveal the differentiated characteristics of residents’ happiness through sustainable consumption across urban and rural areas and different geographical contexts.

2.4. Research on the Relationship between Group Differences and Happiness

Redondo et al. proposed that specific characteristics, termed natural correlates, influence sustainable consumption and happiness. Individuals with stronger natural correlates are more likely to adopt sustainable lifestyles and experience higher happiness. Kuy [27] highlighted differences in debt and wealth accumulation by age but noted that social well-being support does not vary by age or gender. This emphasizes the need to focus on older adults’ mental health and social support systems to enhance their happiness. In addition, Han and Kaiser [28] examined gender differences in happiness, finding that since the 1980s, women’s time-weighted happiness has consistently exceeded that of men. From a broader perspective, Araki [29] identified several key determinants of happiness, including economic growth, generosity, social support, free choice, healthy life expectancy, and corruption. Vezzoli et al. [30] found that individuals’ subjective perception of economic inequality has a notable adverse effect on happiness. In contrast, objective measures of economic inequality do not appear to have a significant impact. Liu et al. [31] further discovered that residents’ participation in the financial market significantly enhances their happiness. Ni and Li [32] highlight that participation in and receipt of old-age security significantly enhances individuals’ subjective happiness. However, the effects of this policy vary between urban and rural areas and across different generations due to individual differences. Pan [33] emphasized that environmental awareness and attention directly influence ecological happiness, while economic factors such as regional development and residents’ income indirectly affect it. In other words, residents’ attention and perception shape the attributes of happiness. Wang et al. [34] argues that overall happiness among older people in China is high; however, the factors influencing happiness differ between rural and urban populations. Individual differences lead to varying perceptions of happiness, necessitating targeted and differentiated policies to enhance the happiness of older adults. These studies indicate that perceptions of happiness vary across age groups, with distinct experiences for the elderly, middle-aged, and young groups. Therefore, there is a need for further exploration of the differences in happiness among these diverse groups.

To this end, based on the review of existing studies, we propose the following hypotheses:

H3: 

There are significant differences in how different age groups respond to sustainable consumption, which affects their happiness.

This hypothesis aims to illustrate the differential characteristics of various age groups regarding happiness and highlight the distinct happiness experiences that each age group derives from engaging in sustainable consumption. Ultimately, this will allow for an assessment of their overall level of happiness.

2.5. Research on the Relationship between Income Levels and Happiness

The role of income in influencing happiness has received considerable attention. Alloush and Wu [35] demonstrated that providing old-age subsidies can boost per capita household income and life satisfaction, significantly enhancing residents’ happiness. Conversely, Ng [36] and Cheung and Leung [37] argued that the correlation between income and happiness is not particularly strong. Frijters et al. [38] found a significant positive correlation between income and happiness. However, Seligman et al. [39] argued that this relationship is not straightforwardly linear but follows an inverted ‘U’ shape. Further research by Zhang and Cai [40] suggested that while absolute income positively affects residents’ happiness and exhibits an inverted ‘U’ shape relationship, this effect diminishes when considering relative income. Other researchers, including Liu et al. [41], Huang [42], Zheng et al. [43], Huo et al. [44], You et al. [45], Liu et al. [46], and Huang and Tian [47], incorporated individual factors affecting residents’ happiness into their research models. Despite identifying variations in the impact of income on happiness, these studies generally found a positive correlation. Sałach-Dróżdż [48] systematically investigated the relationship between wealth inequality and happiness, comparing it with income inequality. Yan and Wen [49] argued that income inequality and corruption significantly reduce the subjective happiness of residents. Additionally, Carver and Grimes [50] suggested that consumption may be a superior measure to income for predicting happiness, proposing that a consumption-based perspective might offer deeper insights into happiness. Meanwhile, Wu and Gao [51] demonstrated that both absolute and relative incomes positively affect residents’ happiness, with relative income having a more significant impact than absolute income. Easterlin [52] notes that happiness responds differently to increases and decreases in income, primarily because individuals assess their income status based on varying considerations during economic expansion and contraction. Additionally, a compelling study by Killingsworth et al. [53] proposes a coherent explanation of the income–happiness relationship, suggesting that happiness increases steadily with logarithmic income increases and even accelerates among the happiest groups. These studies consistently demonstrate that the influence of income levels on factors affecting happiness remains significant, highlighting the need for further exploration of how income levels impact happiness.

To this end, based on the review of existing studies, we propose the following hypotheses:

H4: 

High-income groups are more influenced by the growth effect on happiness improvement than low-income groups.

This hypothesis aims to examine the impact of income levels on the happiness of different demographic groups through the lens of sustainable consumption. We further hypothesize that the happiness enhancement of low-income groups is primarily influenced by the sustainability effect, which will be tested in our empirical analysis.

3. Analysis of the Mechanisms by Which Sustainable Consumption Affects Happiness

Sustainable consumption involves not only addressing the underconsumption of low-income individuals and curbing the overconsumption of high-income groups but, more importantly, aligning with the region’s level of development. Within the constraints of available resources and the ecological environment, sustainable consumption aims to minimize the ecological damage caused by unsustainable consumption practices, enhance the quality of life, and promote intergenerational equity. This involves preventing the exploitation of resources needed by future generations to ensure a fair distribution across time. Consequently, we observe that the impact of sustainable consumption on residents’ happiness primarily results from its influence on economic growth and sustainability.

From the perspective of the economic growth effect, sustainable consumption plays a crucial role in achieving balanced development across the economy and society. It stimulates economic growth and promotes sustained social prosperity and ecological balance. However, some scholars have introduced the ‘happiness paradox’ concept when examining the relationship between economic growth and happiness, suggesting that economic growth may not significantly improve residents’ happiness. For instance, Lu and Zhang [54] and Li and Shi [55] have presented similar arguments, suggesting that economic growth does not significantly enhance happiness. In contrast, Liu and Xu and Shen [56] present an opposing view. They argue that economic growth, a critical driver of increased income and improved living conditions, should enhance residents’ happiness. The ‘happiness paradox’ in China can be attributed primarily to the limitations of the traditional economic growth model. Huang highlights that China’s conventional approach to economic growth has led to new challenges, such as unequal opportunities, a widening urban–rural divide, increasing income inequality, and environmental degradation. These issues have collectively undermined the positive impact that economic growth might otherwise have on residents’ happiness. Thus, the ‘happiness paradox’ is not an inherent flaw of economic growth itself but rather a result of the traditional growth model’s deficiencies. With the implementation of China’s sustainable development strategy, we anticipate that these issues will be addressed, thereby allowing for the positive effects of economic growth on happiness to emerge gradually.

From the perspective of the sustainability effect, we generally observe that as residents’ standard of living improves, their concern for ecological and environmental issues tends to increase. However, research suggests that in more affluent countries with relatively good environmental conditions, residents may be less inclined to make personal sacrifices for environmental protection [57]. We can assess the impact of the ecological environment on residents’ happiness through three key dimensions: health, mood, and income. First, Huang and He and Yang and Zhang [58] highlight that environmental degradation is associated with various health issues, whereas a favorable ecological environment contributes to improved health and happiness. Second, a favorable ecological environment enhances residents’ physical and mental relaxation. Mackerron and Mourato [59] found that residents tend to experience a greater sense of happiness in better ecological conditions. Finally, although temporary income growth achieved through environmental degradation is possible, sustainable economic growth requires preserving a healthy ecological environment. Such environmental stewardship fosters green initiatives that in turn, enhance residents’ income and overall happiness. Despite its potential benefits, we find that several factors constrain the effectiveness of sustainable consumption. Urban–rural disparities, such as the divide between urban and rural areas, unequal opportunities, and the widening gap between the wealthy and the poor, can impede the effectiveness of sustainable consumption. Additionally, the impact of income growth on happiness varies significantly across regions, particularly in those with lower levels of economic development. Sustainable consumption also entails economic transformation, which can impact happiness in the short term by altering industrial structures and employment opportunities. For instance, Helliwell et al. [60] argue that economic transformation often negatively affects happiness. Lin et al. [61] highlight that such transformations may increase unemployment and uncertainty, diminishing happiness. Similarly, Suppa [62] observes that while economic transformation might boost economic performance over the long term, it can also lead to job insecurity, higher unemployment, and greater income inequality during the transition period, adversely affecting happiness.

4. Data Sources and Research Methodology

4.1. Data Sources

This study utilized two primary sources: the Chinese General Social Survey Data 2021 (CGSS2021) and the China Statistical Yearbook. The CGSS2021 dataset selects and analyzes subjective happiness and personal characteristic indicators. In contrast, the China Statistical Yearbook provides data for analyzing macroeconomic factors that impact residents’ happiness.

4.2. Model Configuration

To investigate the relationship between sustainable consumption and residents’ happiness, this study employed a regression model developed based on the framework established by Yang and Zhang. The model is specified as follows.

$$Happines{s}_{ij}={\alpha }_0+{\alpha }_{1}susconsumptio{n}_j+{\alpha }_{2}pri{v}_{ij}+{\alpha }_{3}cti{y}_j+{\epsilon }_{ij}$$

(1)

$Happines{s}_{ij}$ denotes the subjective happiness of the i respondent in province j; $susconsumptio{n}_j$ denotes the proximity of sustainable consumption in the province j; $pri{v}_{ij}$ denotes the personal characteristics of the i respondent in the province j; and $cit{y}_j$ denotes the macro-factors affecting residents’ happiness in the province j.

4.3. Indicator Selection

4.3.1. Measurement of Sustainable Consumption ($susconsumptio{n}_{\mathrm{j}}$)

It is important to note that we observed the value of specific consumption types, such as “green products”, within sustainable consumption. However, our metrics did not include assessments of specific consumption categories due to the research framework, scope, data structure, and availability limitations. Based on this, building on the assessment frameworks developed by Du [63] and Tan and He [64], this study addressed the identified shortcomings by employing the TOPSIS method to evaluate inter-provincial sustainable consumption. We assessed it from three critical perspectives: consumption level metrics, social equity in consumption, and the alignment of consumption practices with environmental sustainability. We list the indicators used in this evaluation in

Table 1. Selection of sustainable consumption metrics.

Standardized Layer	Indicator	Mean	Std.Dev	Min	Max
consumer level	final consumption rate	0.311	0.050	0.216	0.449
	per capita consumption level	2.101	0.677	1.322	4.254
	consumer price index for residents	1.024	0.005	1.015	1.036
	urban and rural residents’ RMB savings deposit year-end balance	3.323	2.367	0.115	9.760
	total retail sales of consumer goods	1.264	1.034	0.075	4.021
social consumption equity	year-end number of persons employed in urban units	1492.613	1138.046	73	5418
	internet broadband access port	3051.771	2099.215	219	8653.2
	state financial resources for education	1118.912	721.675	208.42	3510.56
	government expenditure	6793.015	3544.107	1480.36	17,430.79
	comparison of urban and rural residents’ consumption levels	1.905	0.263	1.510	2.795
harmonization of consumption with the natural environment	electricity consumption	2426.258	1798.539	82	6940
	total urban natural gas supply	504,420.1	445,210.8	4269	1,854,130
	per capita daily domestic water consumption	175.732	48.993	101.3	290.9
	domestic waste disposal	758.432	616.457	62.3	3102.5
	per capita green space	14.355	2.553	9.05	21.02
	public vehicles per 10,000 people	12.523	2.192	8.3	17.25

.

4.3.2. Measures of Subjective Happiness ($Happines{s}_{ij}$)

“Satisfaction” is frequently utilized as a critical indicator for measuring “happiness” [65]. Further research by Ruggeri et al. [66] has strengthened the importance of satisfaction in assessing happiness. Accordingly, we defined “satisfaction” as an individual’s overall evaluation of various aspects of their life, whereas “happiness” referred to their perception of their overall quality of life. Therefore, we used “satisfaction” as a proxy for “happiness”, as it effectively reflected the individual’s subjective happiness [67]. We obtained the 2021 Chinese General Social Survey (CGSS) data, which included samples from 28 provinces, excluding Xizang, Hainan, and Xinjiang. To assess residents’ happiness, the survey asked, “In general, how satisfied are you with your life?” Responses were rated on a scale from 1 to 5, with the options being “very unhappy”, “relatively unhappy”, “neither happy nor unhappy”, “relatively happy”, and “very happy”. Each response was assigned a value ranging from 1 to 5, allowing for a quantitative measurement of subjective happiness.

4.3.3. Personal Characteristics ($pri{v}_{ij}$) and Provincial Macroeconomic Factors ($cit{y}_{\mathrm{j}}$)

We drew personal characteristic data from the 2021 Chinese General Social Survey (CGSS2021) while sourcing provincial macroeconomic factors from the China Statistical Yearbook. Assessing personal characteristics and economic factors followed the methodologies outlined by Li [68] and Li [69]. We quantified individual characteristics based on the questionnaire responses, assigning specific options and values accordingly. For macroeconomic factors, the analysis included metrics such as GDP per capita, level of urbanization, and comparison of consumption levels between urban and rural residents. We provide a detailed description of these factors in

Table 2. Variables definition and descriptive statistics.

Variables	Variables Definition	Mean	Std.Dev	Min	Max
subjective happiness	assign values of 1–5 to questionnaire responses, with very happy rated as 5	4.042	0.734	1	5
sustainable consumption	evaluation using the TOPSIS method	0.361	0.153	0.175	0.632
GDP per capita	GDP per capita (in ten thousand) for the year	8.435	3.883	3.600	16.489
urbanization level	share of the urban population	0.674	0.101	0.522	0.876
population consumption level	per capita consumption (in ten thousand Yuan)	2.101	0.677	1.322	4.254
gender	male = 1; female = 0	0.545	0.498	0	1
age	one full year	51.554	16.891	20	96
religious	religious = 1; not religious = 0	0.078	0.269	0	1
years of education	years required to attain the highest educational degree	11.459	6.438	0	19
political profile	communist party member = 1; other = 0	0.191	0.393	0	1
health	assign values from 1 to 5 to questionnaire responses, with very healthy rated as 5	3.628	0.998	1	5
household registration	non-farm = 1; other = 0	0.268	0.443	0	1
social trust	assign values from 1 to 5 to questionnaire responses, with fully agree rated as 5	3.703	0.932	1	5
sense of fairness	assign values from 1 to 5 to questionnaire responses, with fairness rated as 5	3.436	0.935	1	5
relative income	adoption of the questionnaire: self-assessment of current economic and social level	4.478	1.762	1	10
employment status	involuntary unemployment = 1; other = 0	0.081	0.089	0	1
health insurance	insured = 1; uninsured = 0	0.978	0.146	0	1
pension insurance	insured = 1; uninsured = 0	0.096	0.295	0	1
absolute income	logarithm of total household income	10.456	1.243	2.996	16.019
housing	results of the questionnaire: number of properties	1.324	1.051	0	20
marry	married = 1; unmarried = 0	0.873	0.333	0	1

.

4.4. Data Preprocessing

Due to significant missing data from the Xizang region, this study excluded Xizang from consideration. Samples with responses of “Don’t Know”, “Refuse to Answer”, or “Not Applicable” were removed, resulting in 1240 valid observations.

5. Empirical Analysis

5.1. Overall Regression Results

The dependent variable in this study, subjective happiness ($Happiness$), is an ordinal variable with values ranging from 1 to 5. While it is possible to estimate this variable using an ordered response or logit model, Ferrer-i-Carbonell [70] demonstrated that the Ordinary Least Squares (OLS) estimation produces coefficients that are directionally and significantly consistent with those obtained from these alternative methods. Moreover, the use of OLS in the analysis of happiness is well documented in the literature, with notable examples including He and Pan [71] and Chen et al. [72]. Consequently, we employ OLS for its estimations. We present detailed results in

Table 3. OLS and 2SLS regression results for the full sample.

Variables	OLS				2SLS
	(1)	(2)	(3)	(4)	(5)
sustainable consumption	0.382 *** (2.81)	0.298 ** (2.22)	0.272 ** (2.09)	0.368 * (1.93)	0.368 * (1.94)
relative income		0.106 *** (9.15)	0.066 *** (5.72)	0.065 *** (5.41)	0.064 *** (5.46)
absolute income		0.005 (0.31)	0.003 (0.18)	−0.002 (−0.12)	−0.002 (−0.13)
GDP per capita				−0054 *** (−2.79)	−0.054 *** (−2.81)
urbanization level				1.753 *** (3.37)	1.753 *** (3.39)
per capita consumption				0.060 (0.56)	0.060 (0.56)
gender			−0.033 (−0.86)	−0.028 (−0.72)	−0.028 (−0.72)
age			0.003 * (1.75)	0.002 (1.56)	0.002 (1.57)
religious			−0.115 (−1.62)	−0.126 (−1.45)	−0.126 (−1.46)
years of education			−0.001 (−0.31)	−0.001 (−0.57)	−0.001 (−0.57)
political profile			0.126 ** (4.66)	0.118 ** (2.38)	0.118 ** (2.40)
health			0.113 *** (5.45)	0.115 *** (4.78)	0.115 *** (4.82)
household registration			0.077 * (1.76)	0.049 (1.13)	0.049 (1.14)
social trust			0.088 *** (4.03)	0.089 *** (3.40)	0.089 *** (3.43)
sense of fairness			0.169 *** (7.65)	0.169 *** (6.09)	0.169 *** (6.14)
employment status			−0.001 (−0.00)	−0.036 (−0.33)	−0.036 (−0.33)
health insurance			−0.134 (−1.02)	−0.091 (−0.47)	−0.091 (−0.48)
pension insurance			0.087 (1.31)	0.083 (1.22)	0.083 (1.23)
housing			−0.007 (−0.36)	−0.005 (−0.21)	−0.005 (−0.22)
marry			0.159 ** (2.36)	0.166 *** (2.58)	0.166 *** (2.60)
constant term	3.904 *** (73.31)	3.405 *** (19.78)	2.156 *** (8.75)	1.276 *** (2.95)	1.276 *** (2.98)
sample size	1240	1240	1240	1240	1240

Note: ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

.

Models (1)–(4) in Table 3 present the results of the Ordinary Least Squares (OLS) regression analysis on the impact of sustainable consumption on residents’ happiness. In models (1) through (3), personal characteristics and economic factors are incrementally added to examine their influence, while model (4) includes a robustness regression to ensure the stability of the results. Across all four models, the coefficient of sustainable consumption is significantly positive, indicating that increased sustainable consumption enhances residents’ happiness. In other words, the regression model results support the research hypothesis H1, suggesting that sustainable consumption positively improves residents’ happiness. Regarding local social characteristics, both the level of residents’ consumption and urbanization showed significantly positive effects, with urbanization being significant at the 1% level. This suggests that urbanization during the study period improved infrastructure and provided more employment opportunities, better livelihood security, and a higher quality of life. Conversely, GDP per capita significantly negatively impacted residents’ happiness, suggesting that economic growth may reduce individual happiness. Relative income is significantly positive, indicating that an individual’s financial position relative to others has a more significant impact on happiness than absolute income.

Social insurance and marriage are positively associated with subjective happiness in China, with marriage showing a significantly positive effect at the 1% level in the robust OLS regression. Psychological factors such as social trust, health, and perceived fairness also significantly positively affected happiness, suggesting that higher levels of trust, better health, and perceived fairness enhance happiness. Interestingly, gender, religious beliefs, years of education, employment status, health insurance, and housing did not significantly improve subjective happiness. However, female residents reported higher happiness than male residents (as “male” = 1 and “female” = 0 in the model). Additionally, political affiliation and household registration were positively related to subjective happiness.

5.2. Endogeneity Issues and Two-Stage Least Squares (2SLS)

Although the results from models (1) through (4) suggest that sustainable consumption can enhance residents’ happiness, we must consider potential endogeneity problems. Residents with higher subjective happiness are often more actively involved in public utilities and exhibit a stronger sense of environmental protection. This increased engagement can lead to greater attention paid to nature conservation, governance, and resource sustainability, subsequently influencing local consumption. Many factors shape subjective happiness, and these factors are difficult to measure. These unmeasured factors can introduce endogeneity issues that complicate the relationship between sustainable consumption and happiness analysis. The two-stage least squares (2SLS) method can help address these endogeneity concerns by providing more robust estimates.

To mitigate the effects of endogeneity, this study employs the proportion of value added by the tertiary industry relative to that of the secondary industry and the environmental regulation index as instrumental variables. The environmental regulation index was derived from the measurement method proposed by Zhu et al. [73], utilizing data from the China Environmental Statistics Yearbook and the China Statistical Yearbook. We calculated this index by dividing each province’s annual regional industrial added value (city and autonomous region) by industrial wastewater, industrial solid waste, and industrial sulfur dioxide emissions. This method accounts for the differences in pollutant emissions between regions. Combining these results produces a comprehensive indicator reflecting the level of regional environmental regulation, calculated using the following formula:

$$ER={\displaystyle \frac{1}{3}}(p{x}_{1i}+p{x}_{2i}+p{x}_{3i})={\displaystyle \frac{1}{3}}{\sum }_{l=1}^3\left(n{p}_{li}/{\sum }_{i=1}^n{p}_{li}\right)$$

(2)

In particular, the regional value-added of industry ($p_{li}$) is calculated as the ratio of regional industrial value-added to the absolute value of pollutant emissions due to specific pollutants in provincial units. We have shown that the proportion of value added by the tertiary industry relative to that of the secondary industry, along with the environmental regulation index, exhibits a high correlation with sustainable consumption while maintaining a low correlation with residents’ happiness. This finding preliminarily indicates the feasibility of the selected instrumental variables. Model (5) in Table 3 presents the robust two-stage least squares (2SLS) analysis results. The results show that sustainable consumption remains significantly positive, suggesting that an overall increase in sustainable consumption can enhance residents’ happiness, even after addressing the endogeneity problem. This further confirms that higher sustainable consumption levels effectively boost residents’ happiness.

5.3. Subgroup Regression Results

Due to significant regional and urban–rural disparities in China and a large and varied population base, residents’ age and income status can also influence sustainable consumption patterns. To address these variations, this study conducted regression analyses across different subgroups, including regions, urban and rural areas, age groups (youth, middle-aged people, and older people), and income levels.

Table 4. Subregional, urban, and rural regression results.

Variables	Eastern (1)		Midwest (2)		Rural (3)		Urban (4)
	OLS	2SLS	OLS	2SLS	OLS	2SLS	OLS	2SLS
sustainable consumption	0.168 (0.50)	0.168 (0.51)	2.691 *** (3.11)	2.691 *** (3.16)	0.378 * (1.74)	0.378 * (1.76)	0.294 (0.69)	0.294 (0.72)
relative income	0.061 *** (3.84)	0.061 *** (3.91)	0.071 *** (3.75)	0.071 *** (3.81)	0.070 *** (5.19)	0.070 *** (5.25)	0.049 * (1.94)	0.049 * (2.01)
GDP per capita	−0.048 * (−1.88)	−0.048 * (−1.91)	−0.187 ** (−1.96)	−0.187 ** (−2.00)	−0.059 ** (−2.36)	−0.059 ** (−2.38)	−0.061 * (−1.87)	−0.061 * (−1.93)
urbanization level	1.401 (1.54)	1.401 (1.57)	4.702 *** (2.86)	4.702 *** (2.91)	2.353 *** (3.41)	2.353 *** (3.45)	0.610 (0.67)	0.610 (0.69)
per capita consumption	0.087 (0.67)	0.087 (0.68)	0.023 (0.08)	0.023 (0.08)	−0.024 (−0.19)	−0.024 (−0.19)	0.308 (1.40)	0.308 (1.44)
age	0.003 (1.46)	0.003 (1.48)	0.001 (0.48)	0.001 (0.48)	0.002 (0.86)	0.002 (0.87)	0.005 ** (1.97)	0.005 ** (2.03)
political profile	0.051 (0.78)	0.051 (0.80)	0.207 *** (2.57)	0.207 *** (2.61)	0.077 (1.22)	0.077 (1.24)	0.223 *** (2.73)	0.223 *** (2.81)
health	0.087 *** (2.66)	0.087 *** (2.70)	0.141 *** (4.12)	0.141 *** (4.19)	0.127 *** (4.55)	0.127 *** (4.60)	0.087 * (1.89)	0.087 * (1.95)
social trust	0.053 (1.57)	0.053 * (1.65)	0.126 *** (3.23)	0.126 *** (3.23)	0.085 *** (2.77)	0.085 *** (2.80)	0.112 ** (2.31)	0.112 ** (2.38)
sense of fairness	0.156 *** (4.56)	0.156 *** (4.64)	0.189 *** (4.30)	0.189 *** (4.38)	0.179 *** (5.52)	0.179 *** (5.59)	0.148 *** (2.81)	0.148 *** (2.90)
housing	0.005 (0.32)	0.005 (0.33)	−0.030 (−0.68)	−0.030 (−0.69)	−0.003 (−0.12)	−0.003 (−0.12)	−0.011 (−0.27)	−0.011 (−0.28)
marry	0.155 * (1.73)	0.155 * (1.76)	0.197 ** (2.16)	0.197 ** (2.20)	0.161 ** (2.16)	0.161 ** (2.19)	0.165 (1.31)	0.165 (1.35)

Note: Significant results are presented only for some factors with observed differences in the table. ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

presents the results of these analyses.

Examining the data by regional division ((1) and (2) in Table 4), we find that in the eastern region, the impact of increased sustainable consumption on residents’ happiness is positive but not statistically significant, according to both the OLS and 2SLS models. By contrast, the central and western regions exhibit significantly positive coefficients for sustainable consumption in both models, substantially exceeding those observed in the full sample (Table 3). This suggests that the positive effects of enhanced sustainable consumption are more pronounced in the central and western regions than in the national average. During the study period, increased sustainable consumption markedly improved the residents’ happiness in these regions. This discrepancy arises primarily because the eastern region benefits from substantial economic, consumption, locational, and policy advantages. Consequently, residents in the eastern region tend to prioritize aspects of spiritual happiness over material consumption. Conversely, the central and western regions, characterized by slower development and lower income levels, show a higher propensity to increase income, improve living conditions, and enhance sustainable consumption. Additionally, the relative income levels in these regions are significantly positive, with regression coefficients indicating “Midwest > Eastern”, further supporting the observed regression results. The regional regression results verify research hypothesis H2, suggesting that regional differences significantly affect how sustainable consumption enhances residents’ happiness. Notably, the impact of sustainable consumption is more significant in the central and western regions than in the eastern regions. In other words, the growth effect influences residents’ happiness in the eastern region.

Examining the data from urban and rural areas ((4) and (5) in Table 4), we find that sustainable consumption significantly enhances residents’ happiness in rural areas, as evidenced by higher regression coefficients in both OLS and 2SLS models compared to the national average. In contrast, the regression coefficient for sustainable consumption in urban areas did not achieve statistical significance, suggesting that sustainable consumption does not significantly impact urban residents’ happiness. This discrepancy is primarily due to the influence of income disparity and social fairness on sustainable consumption. In the urban–rural context, the substantial income gap between urban and rural residents drives them to rapidly improve their quality of life and enhance their sustainable consumption to achieve greater material satisfaction and perceived happiness. Conversely, urban residents who have long enjoyed high material conditions and a high-quality living environment may experience diminished returns from further increases in sustainable consumption. The table also reveals that the regression coefficients for relative income and urbanization levels are higher for rural residents than for their urban counterparts, indicating a stronger inclination towards improving income, consumption, and living conditions. The urban–rural regression results strongly support research hypothesis H2, suggesting that regional differences significantly affect how sustainable consumption enhances residents’ happiness. Specifically, the sustainability effect primarily enhances rural residents’ happiness, while the growth effect influences urban residents’ happiness.

Analyzing the data by age group ((6)–(8) in

Table 5. Regression results by age.

Variables	Youth (6)		Middle-Aged (7)		Older People (8)		Low Income (9)		High Income (10)
	OLS	2SLS	OLS	2SLS	OLS	2SLS	OLS	2SLS	OLS	2SLS
sustainable consumption	0.340 (0.81)	0.309 (0.77)	0.395 ** (2.07)	0.368 * (1.94)	0.078 (0.26)	0.070 (0.23)	0.537 (1.61)	0.102 (0.25)	0.488 ** (2.30)	0.488 ** (2.33)
relative income	0.071 *** (2.58)	0.074 *** (2.81)	0.068 *** (5.62)	0.064 *** (5.46)	0.068 *** (4.09)	0.061 *** (3.68)	0.015 (0.48)	−0.029 (−0.59)	0.069 *** (3.54)	0.069 *** (3.58)
GDP per capita	−0.092 ** (−2.19)	−0.095 ** (−2.32)	−0.054 *** (−2.77)	−0.054 *** (−2.81)	−0.058 * (−2.05)	−0.057 ** (−2.06)	−0.074 ** (−2.22)	−0.077 * (−2.01)	−0.046 ** (−2.06)	−0.046 ** (−2.08)
urbanization level	1.623 (1.42)	1.241 (1.10)	1.767 *** (3.38)	1.753 ** (3.39)	0.930 (1.14)	0.999 (1.25)	2.678 *** (3.12)	3.202 *** (2.79)	1.347 ** (2.38)	1.347 ** (2.41)
population consumption level	0.266 (1.17)	0.335 (1.56)	0.061 (0.56)	0.060 (0.56)	0.198 (1.20)	0.182 (1.11)	0.005 (0.03)	0.015 (0.07)	0.048 (0.39)	0.048 (0.40)
political profile	0.057 (0.36)	0.047 (0.31)	0.132 *** (2.77)	0.118 ** (2.40)	0.206 *** (3.51)	0.187 *** (3.24)	0.190 ** (2.12)	0.235 ** (2.03)	0.087 (1.64)	0.087 * (1.66)
health	0.222 *** (3.31)	0.215 *** (3.41)	0.106 *** (4.67)	0.115 *** (4.82)	0.084 ** (2.54)	0.093 *** (2.79)	0.132 ** (3.55)	0.159 *** (3.73)	0.101 *** (3.50)	0.101 *** (3.54)
social trust	0.026 (0.31)	0.014 (0.29)	0.091 *** (3.54)	0.089 *** (3.43)	0.101 ** (2.30)	0.100 ** (2.32)	0.067 (1.59)	0.064 (1.22)	0.108 *** (3.75)	0.108 *** (3.79)
sense of fairness	0.211 *** (3.12)	0.218 *** (3.36)	0.171 *** (6.16)	0.169 *** (6.14)	0.187 *** (4.28)	0.182 *** (4.26)	0.193 *** (4.64)	0.180 *** (3.69)	0.166 *** (5.03)	0.166 *** (5.09)
housing	0.017 (0.82)	0.015 (0.77)	−0.005 (−0.20)	−0.005 (−0.22)	−0.065 * (−1.67)	−0.068 * (−1.77)	−0.050 (−1.36)	−0.062 (−1.44)	0.005 (0.18)	0.005 (0.19)
marry	0.222 *** (2.83)	0.398 ** (2.22)	0.221 *** (3.89)	0.166 *** (2.60)	0.731 (0.98)	0.231 (0.30)	0.213 ** (2.18)	0.310 ** (2.41)	0.117 (1.61)	0.117 (1.63)

Note: Significant results are provided only for some factors with observed differences in the table. ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

), sustainable consumption positively impacts happiness across all age categories. However, this effect was statistically significant only for the middle-aged group. Among the various factors influencing happiness, relative economic status remains predominant, with a pattern of “youth > middle-aged > older people”. Social trust is particularly crucial for middle-aged and older people, with the most significant impact observed among older people. Marriage significantly influences happiness, especially for the youth and middle-aged people; for middle-aged individuals under age 37, marriage emerges as a critical factor in enhancing happiness. Health and fairness are essential across all age groups in China, with their effects on happiness following the patterns “youth > middle-aged > older people” for health and “youth > older people > middle-aged” for fairness. The regression results for age strongly support research hypothesis H3, indicating significant differences in how various age groups respond to sustainable consumption, which affects their happiness. Regarding income perspectives ((9)–(10) in Table 5), sustainable consumption positively affects both low-income and high-income individuals, with a more pronounced impact on high-income groups. This finding suggests that high-income individuals are better positioned to leverage sustainable consumption to enhance their happiness. We find that changes in happiness are influenced by relative income, urbanization level, health, and a sense of fairness. Social trust and marriage significantly impacted happiness for the low-income and high-income groups, respectively. Similarly, the regression results for income validate research hypothesis H4, indicating that the growth effect influences the high-income groups more regarding happiness enhancement. In contrast, the happiness of the low-income groups is primarily affected by the sustainability effect.

5.4. Robustness Tests

5.4.1. Robust Standard Error Test

The robust standard error test evaluates whether the regression results are stable in the presence of potential heteroskedasticity. If the robust standard errors are similar to the benchmark Ordinary Least Squares (OLS) regression results, it suggests that the model’s estimates are relatively robust. According to model (1) presented in

Table 6. Standard errors and regression results of anomalous sample treat.

Variables	(1)	(2)
	Robust Standard Error Test	Handling of Anomalous Samples
sustainable consumption	0.354 * (1.87)	0.349 * (1.82)
relative income	0.064 *** (5.38)	0.066 *** (5.65)
absolute income	0.002 (0.10)	−0.004 (−0.21)
GDP per capita	−0.056 *** (−2.98)	−0.055 *** (−2.83)
urbanization level	1.569 *** (3.08)	1.635 *** (2.90)
per capita consumption	0.090 (0.90)	0.080 (0.83)
gender	−0.039 (−1.01)	−0.030 (−0.78)
age	0.004 *** (3.14)	0.002 * (1.66)
religious	−0.119 (−1.37)	−0.128 * (−1.81)
years of education	−0.02 (−0.59)	−0.002 (−0.58)
political profile	0.119 ** (2.40)	0.120 ** (2.30)
health	0.116 *** (4.79)	0.114 *** (5.54)
household registration	0.045 (1.04)	0.052 (1.17)
social trust	0.088 *** (3.39)	0.088 *** (4.04)
sense of fairness	0.166 *** (5.96)	0.169 *** (7.67)
employment status	−0.056 (−0.54)	−0.037 (−0.18)
health insurance	−0.081 (−0.42)	−0.096 (−0.73)
pension insurance	0.091 (1.35)	0.080 (1.21)
housing	−0.05 (−0.22)	0.013 (0.53)
marry	0.166 ** (2.57)	0.170 ** (2.52)
constant term	1.333 *** (3.09)	1.320 *** (3.65)
sample size	1240	1240

Note: ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

, sustainable consumption significantly positively affects residents’ happiness. The direction and significance of the regression coefficients are consistent with those in Table 3, suggesting that the model is robust to heteroskedasticity and that the results are reliable.

5.4.2. Handling of Anomalous Samples

All variables are Winsor2-tailored at the 1% and 99% quantiles to ensure that outliers do not unduly influence the regression results. According to the regression results of model (2) in Table 6, even after adjusting for outliers in continuous variables, sustainable consumption continues to have a positive and significant effect on residents’ happiness.

5.4.3. Replacement Model Test

To analyze the stability of the model results, this study employs several evaluation models, including robust ordered probit and robust ordered logit models. We present the detailed results in

Table 7. Robustness test results.

Categorization	Sustainable Consumption				Control Variables
	Ordered Probit		Ordered Logit
national	0.657 **	(1.96)	1.260 **	(2.12)	control
eastern	0.335	(0.61)	0.713	(0.72)
central-western	4.616 ***	(2.95)	8.040 ***	(2.93)
urban	0.475	(0.65)	0.513	(0.40)
rural	0.704 *	(1.81)	1.398 **	(2.04)
lower income	0.092	(0.14)	0.244	(0.20)
high income	0.954 **	(2.39)	1.728 **	(2.47)
youth	0.557	(0.77)	0.977	(0.77)
middle-aged	0.657 *	(1.95)	1.260 **	(2.12)
older people	0.201	(0.36)	0.315	(0.31)

Note: ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

.

As shown in Table 7, both ordered probit and ordered logit models indicate that sustainable consumption positively affects residents’ happiness. Notably, the positive impact of sustainable consumption is more pronounced in the central and western regions than in the national average, while the effect in the eastern region remains insignificant. Additionally, the regression results for urban and rural areas reveal that the enhancement of subjective happiness due to sustainable consumption is more substantial among rural and urban residents. Analysis by age group further demonstrates that the impact of sustainable consumption on the subjective happiness of middle-aged individuals is more significant than that of youth and older people. The results in Table 5 are mainly consistent with the direction and significance of the regression coefficients in Table 3 and Table 4, confirming the robustness of the OLS and 2SLS regression results. The robustness test further confirms our proposed four research hypotheses and reiterates that sustainable consumption positively contributes to residents’ happiness.

5.5. Impact Mechanism Test

This study examines how sustainable consumption impacts residents’ happiness by analyzing two main effects: growth and sustainability. The growth effect is realized through improvements in economic status, whereas the sustainability effect is achieved through enhancements in urban environmental quality and green development. To assess these effects, this study used relative income to indicate economic status, resource utilization (measured by general solid waste recycling rates), and residents’ green living standards (represented by per capita green space in parks) to capture sustainability. Robust OLS regression was employed to control for both individual and regional characteristics. We present the detailed results of this analysis in

Table 8. The results of the analysis of the impact mechanisms of sustainable consumption.

Indicator	National (1)	Eastern (2)	Central-Western (3)	Urban (4)	Rural (5)	High Income (6)	Low Income (7)
relative income	0.064 *** (5.50)	0.063 *** (4.07)	0.066 *** (3.57)	0.049 ** (1.97)	0.069 *** (5.20)	0.069 *** (3.57)	0.034 (0.71)
resource utilization	0.084 ** (2.39)	0.097 (0.66)	0.135 * (1.74)	0.114 * (1.65)	0.078 * (1.94)	0.083 ** (2.10)	0.117 (1.64)
green living	0.334 * (1.78)	0.759 (1.36)	0.374 (1.30)	−0.294 (−1.16)	0.417 * (1.80)	0.098 (0.49)	0.687 * (1.80)
sample size	1240	625	616	332	909	910	331
R2	0.206	0.203	0.219	0.240	0.198	0.169	0.204

Note: Control for individual characteristic variables and certain economic variables in the model; their regression results are not presented. ***, **, * Denote significance levels at 1%, 5%, and 10%, respectively.

.

At the national level, relative income, resource utilization, and green living standards significantly impacted residents’ happiness. This suggests that sustainable consumption’s growth and sustainability effect positively contribute to residents’ happiness.

From a regional perspective, the analysis reveals that relative income, resource utilization, and residents’ green living standards positively influence residents’ happiness in the eastern region. Notably, relative income had a particularly significant effect. In contrast, resource utilization and residents’ green living standards positively affected subjective happiness in the central and western regions. This indicates a greater emphasis in these regions on improving relative income and resource recycling than in the eastern region. The significance and magnitude of the regression coefficients for sustainability indicators differed between the two regions. Specifically, the coefficient for resource utilization was higher in the central and western regions than in the eastern region, suggesting a stronger focus on resource recycling. Meanwhile, the eastern region emphasizes enhancing the quality of urban infrastructure and green spaces.

From the perspective of urban and rural areas, both the growth and sustainability effects of sustainable consumption positively impact rural residents’ happiness. However, only the growth effect was significant for urban residents, whereas the sustainability effect did not show a meaningful impact. Notably, the estimated coefficient for green living is negative, suggesting that the sustainability effect has a detrimental impact on urban residents’ happiness. This indicates that in urban areas, the contribution of green living to happiness is restricted and potentially counterproductive.

This study categorizes the population into high-income and low-income groups based on relative income to further validate the above propositions. The analysis revealed that relative income and resource utilization significantly enhance the happiness of high-income individuals. In contrast, for low-income groups, only the sustainability effect associated with green living positively impacted happiness, while the growth effect was insignificant.

6. Conclusions, Significance, and Prospects for Future Research

6.1. Conclusions of the Study

This study comprehensively analyzes the relationship between sustainable consumption and residents’ happiness, utilizing data from the Chinese General Social Survey 2021 (CGSS2021). The findings reveal vital insights: (1) sustainable consumption positively influences residents’ happiness. This is further supported by related studies conducted by Chéron et al. [13] and Sameer et al. [18], which concluded that sustainable consumption positively impacts happiness. (2) Regionally, the benefits of increased sustainable consumption are notably pronounced in the central and western regions, significantly enhancing residents’ happiness more than the national average. Conversely, the impact in the eastern region is less pronounced and statistically insignificant. Consistent with the studies of Elburz et al. [21], Nasser and Fakhroo [22] and Counted et al. [25], these findings suggest that happiness varies among residents in different regions, indicating the need for differentiated policies to enhance overall happiness. (3) Regarding urban versus rural areas, sustainable consumption significantly improves rural residents’ happiness, with effects exceeding the national average. In contrast, the impact on urban residents is negligible. This aligns with the studies by Zhang et al. [24], Zhang and Wan [20], and Tassinari et al. [26], which emphasize the importance of incorporating the urban–rural dimension to comprehensively analyze the heterogeneity of happiness across different regions, thereby yielding more practically actionable research results. (4) The age-related analysis indicates varying factors influencing happiness: health is a significant concern for younger individuals, marital status is crucial for middle-aged adults, and housing is the primary concern for older people. This finding echoes the research of Kuy [27], Redondo et al. [11] and Wang et al. [34], which emphasizes analyzing the differences among various age groups to propose targeted initiatives for enhancing happiness. (5) Income levels also play a critical role in happiness, with increased income having a more substantial effect on high-income groups than low-income groups. This suggests that the impact of income on happiness is more pronounced among high-income groups, highlighting significant disparities in satisfaction levels across different income brackets. Studies by Alloush and Wu [35], Easterlin [52], Wu and Gao [51] and Killingsworth et al. [53] provide relevant support, agreeing that fluctuations in income significantly impact happiness across different groups.

6.2. Significance of the Study

From a theoretical perspective, this study reveals several key findings: First, sustainable consumption significantly enhances happiness in the central and western regions, challenging the previous notion that the impact of sustainable consumption on happiness is uniform. Second, sustainable consumption has a more substantial effect on happiness in rural areas than in urban areas, offering a new perspective on how regional and urban–rural differences affect the outcomes of sustainable consumption. Third, the study highlights differences in happiness concerns across age groups, enriching the understanding of how various life stages influence happiness. Lastly, the differential impact of income levels on happiness challenges the oversimplified theory that treats all income levels equally. These findings provide a theoretical foundation for developing sustainable consumption policies tailored to regional, urban–rural, age, and income-related differences.

In practical terms, based on these findings, sustainable consumption’s impact on happiness varies significantly across regions, urban–rural areas, age groups, and income levels. These disparities underscore the necessity of targeted policy interventions: (1) Education and guidance on sustainable consumption should be enhanced. The government should intensify efforts to promote sustainable consumption through increased public awareness and education. This includes integrating sustainable consumption principles into school curricula, community programs, and public campaigns. Additionally, the government disseminates green consumption guidelines and encourages enterprises to develop and market environmentally friendly products. (2) Regionally differentiated policies should be implemented. The government should tailor policies to the specific needs of different regions. In the central and western regions, where sustainable consumption has a more substantial impact, subsidies and tax incentives could be used to support the adoption of green products, thereby improving residents’ happiness. Conversely, in the eastern region, policies should focus on fostering cultural and spiritual enrichment to enhance residents’ satisfaction. (3) Sustainable consumption in rural areas should be promoted. Increased support for rural green consumption is essential. The government should enhance the market infrastructure and availability of green products in rural areas. Concurrently, improving infrastructure and public services will contribute to a higher quality of life and greater happiness for rural residents. (4) Age-specific policies should be developed. Policy measures should address the needs of different age groups. Young people would benefit from health education and fitness services, middle-aged individuals from family support and marriage counseling, and older people from improved housing options and residential security. (5) Income disparities should be addressed. To bridge income gaps, the government should focus on narrowing income inequality and promoting inclusive prosperity. For low-income groups, enhancing social security and employment support is crucial for ensuring a basic standard of living, thereby improving overall happiness.

6.3. Prospects for Future Research

This study has several limitations and suggests directions for future research. First, based on data from the Chinese General Social Survey 2021 (CGSS2021), which is representative, the data only capture a snapshot of a specific time. Consequently, the data do not fully reveal the long-term dynamics between sustainable consumption and happiness. Future studies should incorporate longitudinal studies to monitor changes in consumption behavior and happiness over time, thereby elucidating the long-term effects of sustainable consumption on residents’ happiness. Second, although we identified a correlation between sustainable consumption and residents’ happiness, other potential factors, such as socioeconomic conditions and cultural environment, may also influence this relationship. Future studies should investigate these factors further to provide a more comprehensive understanding of their mechanisms, which would strengthen the foundation for policy formulation and enhance the effectiveness of sustainable consumption initiatives in improving residents’ happiness. Third, this study primarily relies on subjective questionnaire data to measure sustainable consumption, which may not fully capture its diversity and complexity. Future studies should broaden the indicator system to include green product consumption and resource recycling. This approach will enable a more thorough assessment of the multidimensional impacts of sustainable consumption behaviors on happiness. Finally, future studies could benefit from designing policy intervention experiments to evaluate the effects of specific policies on sustainable consumption and happiness. Such experiments would help identify the most effective policy measures, guiding residents toward sustainable consumption, enhancing overall happiness, and promoting sustainable social development.