Research on the Dynamic Fuzzy Evaluation and Promotion Strategy of Green and Low-Carbon Lifestyle Among the Chinese Public

Abstract

The popularization of a green and low-carbon lifestyle among the public is a key link to achieve the goals of a carbon peak and carbon neutrality. By conducting an extensive questionnaire survey, this paper focuses on the current situation of a green and low-carbon lifestyle among the Chinese public, and deeply explores the cognition and practical levels of a green and low-carbon lifestyle among the public. Based on the entropy weight method, a set of evaluation index systems that can comprehensively reflect the public’s green and low-carbon lifestyle has been constructed, and the core factors influencing the public’s green and low-carbon lifestyle behaviors have been extracted. At the same time, a dynamic fuzzy evaluation model has been constructed to predict and analyze the development trend in the public’s green and low-carbon lifestyle. The research results showed that the Chinese public has achieved initial results in promoting a green and low-carbon lifestyle, showing a good development trend. To further promote the popularization of a green and low-carbon lifestyle, this paper proposes countermeasures and suggestions such as strengthening the tripartite cooperation among the government, enterprises, and society, improving the market incentive mechanism, and strengthening publicity and education. This paper not only has certain theoretical significance, but also provides practical implications for the global response to climate change.

1. Introduction

In the context of increasingly severe global environmental change, it has become a crucial task to advocate for the public to practice a green and low-carbon lifestyle. The Paris Agreement, adopted by the 21st United Nations Climate Change Conference, sets the goal of keeping the global average temperature rise within 2 °C above pre-industrial levels by the end of the 21st century, and striving to limit it to 1.5 °C. To achieve this goal, carbon emissions per capita need to be reduced to 2–2.5 tons of CO² equivalent by 2030, with further reductions by 2050 [1,2].

The 2020 Emissions Gap Report adopted by the United Nations Environment Program further reveals that households currently account for approximately two-thirds of global greenhouse gas emissions, with food consumption accounting for 20%, residential energy consumption for 19%, and private household transportation for 17% [3]. These data fully highlight the significant impact of public lifestyles on climate change. Specifically, the annual per capita carbon footprint of American households range from 17.7 to 20.6 tons of CO₂ equivalent, with the residential and transportation sectors contributing 53% to 66% of the household carbon footprint [4]. From the perspective of China’s carbon emission structure, the direct energy consumption by the public accounts for 26% of the total energy consumption, and the resulting carbon emissions account for more than 30% [5]. According to a recent study by the Chinese Academy of Sciences, carbon emissions from industrial processes and residential lives account for as much as 53%, which shows that promoting the public to practice a green and low-carbon lifestyle is an inevitable choice in addressing climate change.

Pollutant emissions caused by energy consumption have become the main source of air and water pollution. Reducing energy consumption in daily life can not only significantly reduce the emissions of harmful gases and sewage and alleviate the pressure of ecological environment deterioration, but also help to promote the harmonious coexistence of human beings and nature. Some scholars have proposed that the reduction in energy consumption in daily life depends on people’s awareness of ecological balance and their alertness to the energy crisis. Building energy conservation, public resource utilization, and living consumption control are important ways to save energy [6].

China has clearly stated the goal of “striving to carbon peak by 2030 and striving to achieve carbon neutrality by 2060” [7]. The report of the 20th National Congress of the Communist Party of China further emphasizes that Chinese-style modernization is the modernization of harmonious coexistence between human beings and nature and achieving a carbon peak and carbon neutrality is an important part of this process. The green and low-carbon transformation and development of the economy and society led by China’s “dual carbon” goal is becoming a systemic transformation related to high-quality and sustainable economic and social development, comprehensively reshaping the trajectory of economic and social development. In this context, the popularization and promotion of the public’s green and low-carbon lifestyle plays an essential role in achieving the goals of a carbon peak and carbon neutrality.

2. Theoretical Foundation and Literature Review

A green and low-carbon lifestyle is a kind of life concept and practice mode aiming at reducing carbon emissions, saving resources, and protecting the environment. Its dissemination and practice are not only supported by a variety of theories, but domestic and foreign scholars have also conducted a large number of research projects and achieved fruitful research results.

2.1. Relevant Theoretical Support

The first theory is Social Contagion Theory. In studying the spread of perceptions, attitudes, and behaviors among people, Reddel Fritz first proposed the concept of “social contagion”. Social contagion refers to a situation where people’s perceptions or behaviors will imitate each other, and individuals have the tendency to follow the crowd and conform to the group’s norms. Because of imitation or conformity, certain perceptions or behaviors spread through social relationships as if they are contagious. In the context of promoting a green and low-carbon lifestyle, a classic case of social contagion is the neighbor effect of household solar energy. Researchers’ quantitative analysis showed that the higher the proportion of a resident’s neighbors who installed a household solar power generation system, the more likely the resident was to install it [8]. Other studies have shown a similar pattern of “social contagion” in the spread of energy-efficient product purchases. But as the researchers point out, the social contagion of behavior is premised on the idea that people are more likely to be influenced by the behaviors of those around them, or those who are perceived to be similar to themselves [9]. In other words, the diffusion of behaviors depends on strong social relations. Centola, a sociologist at the University of Pennsylvania, distinguished between the spread of behavior and the spread of information or perceptions. He believes that the spread of information is similar to influenza, and this can be achieved through simple transmission [10]. However, the dissemination of new behaviors is not easy. Adopting a new behavior often involves financial, psychological, and reputational risks. These factors constitute complex transmission. People need to be exposed to multiple reinforcement sources before adopting a new behavior to ensure feasibility [11]. The experiments of Centola further showed that the diffusion of behavior requires multiple overlapping links connecting clusters of individuals to achieve the best propagation effect [12]. Therefore, to spread the concept of a green and low-carbon lifestyle among the public, it is suggested to create a good green and low-carbon lifestyle atmosphere in the community, create demonstration cases, and spread it among the residents through social relationship networks.

The second theory is Behavior Change Theory. Different schools of behavior change theories, such as social learning theory [13], metrological behavior theory [14], and behavior stage change theory [15], analyze the motivation and process of behavior changes from different perspectives. These theories indicate that behavioral change requires the combination of multiple agents and the external environment [16]. In the unprepared stage, there is no intent to make some changes in the near future due to the lack of awareness of a green and low-carbon lifestyle. In the hesitation stage, the intention to make a change arises because of an understanding of the benefits and disadvantages that change can bring. In the preparation stage, people begin to plan for changes and will act on them. For example, people may start planning to purchase energy-saving electrical appliances, or prepare to learn about garbage classification. In the action stage, people begin to travel by public transportation and implement water-saving and power-saving behaviors in their daily life. In the maintenance stage, people will actively continue the new behavior until the new behavior becomes a habit, and the whole process of behavior change is completed. Researchers who investigate behavior change also point out that lifestyle change requires not only individual-level action, but also requires systematic change, both of which are indispensable [17]. The theory of behavior change indicates that when promoting a green and low-carbon lifestyle, we should take targeted measures according to the characteristics of different stages of behavior change in order to achieve greater results. For example, for people in the hesitant stage, more cases about the advantages of a green and low-carbon lifestyle can be provided to help them make their decisions to make changes.

2.2. Literature Review

In the face of the increasingly severe global climate problems, more and more research attention has been paid to the green and low-carbon lifestyle.

Some scholars have analyzed the challenges of implementing a green and low-carbon lifestyle, including the lack of the concept of a green and low-carbon lifestyle and specific lifestyle practices [18]. As a comprehensive concept encompassing behavior, cognition, and context, lifestyle changes are not only influenced by individual choices, but also significantly constrained by social and material contexts. A broader analytical perspective is needed to achieve effective low-carbon interventions. Environmental factors play a key role in shaping lifestyles and may lock in unsustainable behaviors or promote sustainable behaviors. Achieving the transition to a green and low-carbon lifestyle requires interventions from a broader social, economic, and environmental perspective [19].

The influencing factors of the public’s practice of a green and low-carbon lifestyle also involve many aspects, such as individual characteristics, social and economic background, environmental cognition, and behavioral motivation. Internal factors related to low-carbon behavior is not closely linked with demographic variables, whereas the external factors related to low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income [20]. Many papers have pointed out that education is an important factor affecting the green and low-carbon lifestyle. For example, Hu et al. (2024) [21] emphasized that people with a higher education are more likely to adopt a green and low-carbon lifestyle, which is related to their awareness of environmental issues and their acceptance of new technologies and ideas. People with higher education levels are more inclined to live a healthy, low-carbon lifestyle [22]. Gender, age, and socioeconomic status also have significant impacts on the formation of a green and low-carbon lifestyle. Most studies have concluded that women, young people, and those with a higher socioeconomic status are more likely to adopt a green and low-carbon lifestyle. Geographic location and resource support are also important factors affecting the green and low-carbon lifestyle. For example, people living in or around big cities are more likely to adopt a green and low-carbon lifestyle, which is related to the better urban infrastructure in cities and easier promotion of a green lifestyle. Wang et al. (2022) [23] stated that in rural areas of ecologically fragile energy areas, factors such as green energy saving technology, traditional energy transformation, and energy efficiency are the keys to achieve green development in rural areas. Environmental attitude is one of the key factors influencing the green and low-carbon lifestyle [24]. Andersson (2016) argued that situational factors (e.g., socioeconomic and geographical) and motivational factors (e.g., environmental attitudes) play an important role in influencing household greenhouse gas emissions [25]. Xiao et al. (2021) [26] further pointed out that endogenous dynamics, rational selection, and sustainable development capacity are also important factors affecting the green and low-carbon lifestyle. These external factors contribute to the formation of a green and low-carbon lifestyle by affecting an individual’s environmental cognition and behavioral motivation.

Policies and regulations play an important role in promoting a green and low-carbon lifestyle. The study of Azalia et al. (2016) showed that policies and regulations are one of the key factors affecting the adoption of a low-carbon lifestyle [27]. Roy et al. (2017) [28] also found that although affluence is the biggest contributor to carbon emissions, the implementation of policies such as environmentally sensitive behavior, clean technology, and green energy structure can help reduce the negative environmental impact. People with lower incomes who did not participate in lifestyle changes responded more positively to energy-saving “avoidance” behaviors [29]. Media promotion and corporate social responsibility are also important factors influencing the green and low-carbon lifestyle. Cai et al. (2022) [30] pointed out that environmental factors such as climate change, public media, and corporate social responsibility have positive impacts on entrepreneurs’ low-carbon behavior. There is a significant relationship between green consumption behavior and purchasing motivation, channel factors, consumer innovation, price factors, and other influencing factors, while the influence of incentive factors and psychological factors are not significant [31]. By encouraging and standardizing the behavior of individuals and enterprises, and with the help of media publicity and guidance, the popularization of a green and low-carbon lifestyle for the public can be effectively promoted.

Communities play an irreplaceable role in promoting a green and low-carbon lifestyle. Some studies have indicated that communities contribute to the realization of low-carbon lifestyles by exploring and applying green practices [32]. As an important place in an individual’s life, low-carbon practices in the community can directly affect individual lifestyle choices. The community’s adoption of low-carbon technologies, strategies, and lifestyle innovation also provides strong support for a green and low-carbon lifestyle. For example, rational environmental design, green roof systems, the use of renewable energy, and changes in living patterns and energy-related behaviors are all important ways to achieve a low-carbon lifestyle [33]. However, the study by Lai and Wan (2023) [34] found that although residents showed a strong awareness of low-carbon lifestyles, their actual level of participation was low. Therefore, educational guidance should play a positive role in promoting green and low-carbon lifestyle in communities.

In terms of promoting the practice of a green and low-carbon lifestyle, scholars have proposed various strategies. For example, this can be achieved by promoting end-use green energy consumption, implementing green cost guidance, and behavioral training mechanisms to promote green low-carbon production and lifestyle changes [21]. Promoting a shared service economy, sharing clothes and equipment, reducing motorized transportation, and a vegan diet are also effective ways to reduce carbon footprints [35]. Energy-saving technologies, traditional energy transformation, and a green lifestyle are the keys to achieve green development in rural areas with ecologically fragile energy resources [23].

In terms of effect evaluation, scholars have established a systematic evaluation method for a regional green and low-carbon development level, including evaluating the overall green and low-carbon development level of the object and analyzing the coupling and coordination of the two subsystems of green development and low-carbon development [36]. The low-carbon city pilot is conducive to establishing a low-carbon industrial system, advocating a low-carbon lifestyle, and establishing a low-carbon evaluation system, which will play a positive role in promoting the green and low-carbon development level of cities [37]. These studies contribute to the scientific evaluation of the effects of a green and low-carbon lifestyle, and provide a basis for policy formulation and practical guidance.

In summary, although current academic research on green and low-carbon lifestyle behaviors among the public has made certain progress, it primarily focuses on influencing factors and effect evaluations, predominantly adopting a static qualitative research paradigm. There is a lack of follow-up research on the dynamic development trends of behavioral evolution. Driven by the “dual-carbon” goals, breaking through the limitations of traditional research to enhance the promotion efficiency of green and low-carbon behaviors has become an urgent academic proposition. This study introduces a dynamic fuzzy evaluation model, conducting in-depth discussions on the development trends of green and low-carbon lifestyle behaviors among the public from a novel perspective, thereby breaking through the limitations of traditional static research. Based on large-scale questionnaire survey data, this study identifies core factors influencing green and low-carbon lifestyle behaviors among the public and predicts future development trends by constructing a dynamic fuzzy evaluation model. This provides strong data support for government departments to formulate differentiated dynamic intervention strategies, promoting the transition to a green and low-carbon lifestyle from an experience-driven paradigm to a data-driven one. This study not only enriches theoretical research in the field of green and low-carbon lifestyles but also provides valuable practical experience for the global response to climate change, possessing significant theoretical value and practical significance.

3. Empirical Strategy and Data Sources

This study employed an empirical analysis through a large-scale questionnaire survey, with emphasis on randomness and diversity in the sample selection, and scientific rigor and rationality in the questionnaire design.

3.1. Questionnaire

On the basis of an extensive literature analysis, combined with the current situation of the green and low-carbon lifestyle of the Chinese public, the key information about the green and low-carbon lifestyle of the public was extracted through expert interviews, and the questionnaire was carefully developed to understand the public’s perceptions and practice of a green and low-carbon lifestyle. As shown in Appendix A, the questionnaire also set up open-ended questions to encourage respondents to provide personalized insights and enhance the depth and breadth of the questionnaire. Before the formal questionnaire, a small-scale pre-investigation and expert interview were conducted to make the questionnaire more scientific and reasonable.

The scientific method of extensive coverage and random sampling was adopted in the selection of survey samples. Relying on the summer practice of Zhejiang University of Science and Technology students returning to their hometown, during the period from July to August 2024, more than 10,000 university students were randomly selected from more than 20,000 students as the survey targets, and then spread to relatives and friends through their social networks. The respondents selected for this survey were required to be 18 years old or above to ensure that they had basic cognitive and judgment abilities and could accurately understand the content of the questionnaire and provide reasonable answers. At the same time, no specific restrictions were placed on the cognitive levels of the respondents, so as to obtain the views and practices of people at different cognitive levels regarding green and low-carbon lifestyles and more comprehensively reflect the overall situation of the public. The Wenjuanxing platform 2.2.6 was used to distribute questionnaires and set up anti-cheating mechanisms such as specifying the answering time and enabling mandatory logical jumps. With the highly developed and accessible Internet, online questionnaire surveys can achieve extensive coverage. University students have extensive social networks and can guide their relatives and friends of different ages to participate, thus balancing the representativeness in terms of age. The questionnaires are designed to be simple and easy to understand, catering to people at different educational levels. In terms of the survey areas, there is a pattern of relatively concentrated distribution in the eastern and central regions, with coverage of the main areas in the western region, and the distribution ratio between urban and rural areas is coordinated. The sample structure is reasonable and representative, effectively supporting comparative analyses across different regions and various groups.

After the data were collected, Python 3.13 software and the cross-validation method were used to check whether there was an overfitting problem in the model. The questionnaire data were divided into a training set and a validation set. The generalization ability of the model was calculated using k-fold cross-validation. It was found that the difference in accuracy between the training set and the validation set of the questionnaire indicators was higher than 0.1, indicating that the model had an overfitting problem and poor generalization ability. After manually screening the abnormal data and removing the invalid samples with obvious false responses or consistent questionnaire answers, when verifying again, the difference in accuracy between the training set and the validation set was less than 0.1. The generalization ability of the model is stable, there is no overfitting problem, and the accuracy and reliability of the survey data are effectively improved. The scope of the survey covers the whole territory of China, and the samples are fully extensive and representative. A total of 11,790 valid samples were finally obtained.

3.2. Reliability and Validity Test

This study was rigorously tested with the help of SPSS 29.0.1 statistical software. The reliability coefficient method was adopted to test the reliability and the value of 0.6 or above indicates a good reliability. From the inspection of each dimension in

Table 1. Reliability test results.

Evaluation Dimension	Reliability Coefficient Value
Cultural Dimension	0.933
Behavioral Dimension	0.934
Technological Dimension	0.935
Environmental Dimension	0.932

, the coefficient values of all dimensions are far higher than the critical standard of 0.6, and the overall values are within the high reliability interval of 0.932–0.935. In the early stage of designing this questionnaire, based on the public low-carbon theory and the relevant literature, a systematic and comprehensive evaluation index system for public low-carbon behaviors was constructed. Several experts were invited to calibrate the items, and those items with a low correlation to the evaluation indicators were removed. The remaining items highly focus on the core content of each evaluation indicator in terms of content, with a rigorous logical framework and a high degree of correlation among the items. This has made the reliability coefficients of all dimensions of the questionnaire higher than 0.9. At the same time, it also indicates that the items of the questionnaire have a stable and consistent structure in measuring the indicator variables, providing a reliable quantitative basis for the research. The factor analysis method was used to assess the validity of the questionnaire. As shown in

Table 2. Validity test results.

KMO Test	0.953
Bartlett’s Test of Sphericity	Approx. Chi-Square	98,021.402
	Degrees of Freedom	120
	Significance	0.000

Note: in the context of statistical tables, the significance level is often abbreviated and presented as a very small number close to zero when it indicates a highly significant result. Here, “0.000” is commonly used to represent significance at the p < 0.001 level.

, the KMO (Kaiser–Meyer–Olkin) value was very close to 1, indicating that the questionnaire is very suitable for factor analysis. In addition, the significance level was 0.000, far below the critical value of 0.05, which met the requirements of the Bartlett sphere test, confirming a good structural validity of the questionnaire. Therefore, the questionnaire used in this study is not only reliable, but also has excellent validity, which lays a good foundation for the subsequent data analysis.

3.3. Construction of Evaluation Factor Indicators

The weights of the indicators were calculated using the entropy weight method based on the survey data. The entropy weight method is an objective weighting method based on the information entropy theory, which is suitable for determining the weights of multi-dimensional evaluation indicators. The smaller the information entropy, the higher the degree of variation in the indicator data, and the higher the weight of the indicator. Conversely, the lower the information entropy, the lower the degree of variation in the indicator data, and the lower the weight of the indicator. The specific steps are as follows.

The entropy weight method is an objective weighting method based on the information entropy theory, which is suitable for determining the weights of multi-dimensional evaluation indicators. The smaller the information entropy, the higher the degree of variation in the indicator data, and the higher the weight of the indicator. Conversely, the weight of the indicator is lower. The specific steps are as follows.

• Step 1. Data preprocessing

Suppose there are $n$ evaluation objects and $m$ evaluation indicators, and an original data matrix $X_{ij}={\left(x_{ij}\right)}_{n\times m}$ is constructed, where $x_{ij}$ represents the value of the $j$-th indicator of the $i$-th object ($i=1,2,\dots ,n$, $j=1,2,\dots ,m$). If the evaluation indicator is a negative indicator, it is converted into a positive indicator by using ${x^{\prime }}_{ij}=\max \left(x_j\right)-x_{ij}$ or ${x^{\prime }}_{ij}=1/x_{ij}$ ($x_{ij}>0$).

• Step 2. Data standardization

The normalization method is used to standardize the data of positive indicators, and a standardized data matrix $Y_{ij}={\left(y_{ij}\right)}_{n\times m}$ is established, where $y_{ij}={\displaystyle \frac{{x^{\prime }}_{ij}-\min \left({x^{\prime }}_j\right)}{\max \left({x^{\prime }}_j\right)-\min \left({x^{\prime }}_j\right)}}$, $y_{ij}\in \left(0,1\right]$.

• Step 3. Calculation of indicator entropy value

The entropy value $e_j=-{\displaystyle \frac{1}{\ln n}}{\displaystyle {\sum }_{i=1}^n{p}_{ij}\ln p_{ij}}$ of the $j$-th indicator is calculated, where $p_{ij}={\displaystyle \frac{y_{ij}}{{\displaystyle {\sum }_{i=1}^n{y}_{ij}}}}$, ${\displaystyle {\sum }_{i=1}^n{p}_{ij}}=1$.

• Step 4. Calculation of indicator weight

The weight $w_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^m{d}_j}}}$ of the $j$-th indicator is calculated, where $d_j=1-e_j{d}_j$, ${\displaystyle {\sum }_{j=1}^m{w}_j}=1$.

The evaluation element indicators are constructed for the current situation of the Chinese public’s green and low-carbon lifestyle, which include four first-level indicators and sixteen s-level indicators. Please refer to

Table 3. Evaluation dimension of the status quo of green and low-carbon lifestyle among Chinese public.

Indicators	Cultural Dimension (A1)	Behavioral Dimension (A2)	Technological Dimension (A3)	Environmental Dimension (A4)
Weight (%)	23.1	27.33	23.87	25.7

,

Table 4. Evaluation elements of cultural dimension.

Indicators	Conceptual Identification (A11)	Behavioral Cognition (A12)	Influence of Friends and Family (A13)	Cultural Environment (A14)
Weight (%)	5.07	5.59	6.13	6.31

,

Table 5. Evaluation elements of behavioral dimension.

Indicators	Public Transportation (A21)	Waste Sorting (A22)	Use of Disposable Products (A23)	Clean Plate Campaign (A24)
Weight (%)	6.23	6.59	8.97	5.54

,

Table 6. Evaluation elements of technological dimension.

Indicators	Low-Carbon Technology (A31)	Low-Carbon Products (A32)	Harmless Waste Disposal (A33)	Comprehensive Waste Utilization (A34)
Weight (%)	4.48	6.36	6.50	6.53

and

Table 7. Evaluation elements of environmental dimension.

Indicators	Air Quality (A41)	Sewage Treatment (A42)	Green Coverage (A43)	Solid Waste Disposal (A44)
Weight (%)	6.40	6.57	6.21	6.52

and Figure 1 for details.

4. Construction of Dynamic Fuzzy Evaluation Model

Fuzzy evaluation analysis is a data analysis method that reveals uncertain rules. It contains static and dynamic fuzzy evaluation analysis methods. The static fuzzy evaluation analysis method evaluates the state of an object at a certain time, which is commonly used in analytic hierarchy analysis and the Delphi method. The dynamic fuzzy evaluation analysis method evaluates the state of an object at a specific time and predicts the future evolution trend. The specific steps are described as follows.

Step 1. Construct an evaluation system. Define the set of primary indicators for the object as follows, and the set of secondary indicators as $D_v=\left\{D_{v1},D_{v2},\dots ,D_{vk}\right\}$ ($v=1,2,\dots ,t$, $1<k\le p$, $t$, $p$ are the numbers of primary indicators and secondary indicators, respectively).

Step 2. Determine the evaluation level and threshold value. Excellent, good, fair, poor, and very poor are used as the evaluation grades, and the specific evaluation grade range of each grade is shown in

Table 8. Evaluation of grad thresholds.

Evaluation Grade	Excellent	Good	Fair	Poor	Very Poor
Score Threshold	95	90	80	60	50

. Scores less than 50 (including 50) are very poor, 50 to 60 (including 60) are poor, 60 to 80 (including 80) are fair, 80 to 90 (including 90) are good, and 90 to 100 are excellent.

Step 3. Construct the membership function. The membership degree is used to comprehensively reflect the degree to which the research subject is subordinate to the fuzzy evaluation grade. According to the fuzzy evaluation theory, within the range of any fuzzy evaluation level, different evaluation scores are different from the corresponding evaluation level of the membership level and constructing the membership function is a general method to determine the membership level. To prevent score values from being affected by the thresholds, membership was determined using a piecewise linear membership function.

Step 4. Build the evaluation matrix. The evaluation matrix $R$ is constructed by $({\overleftarrow{g}}_{ij},{\overrightarrow{g}}_{ij})$ ($i=1,2,\dots ,m$, $j=1,2,3,4,5$) of the secondary index set.

$$R=\left(\begin{array}{ccccc}({\overleftarrow{g}}_{11},{\overrightarrow{g}}_{11})& ({\overleftarrow{g}}_{12},{\overrightarrow{g}}_{12})& ({\overleftarrow{g}}_{13},{\overrightarrow{g}}_{13})& ({\overleftarrow{g}}_{14},{\overrightarrow{g}}_{14})& ({\overleftarrow{g}}_{11},{\overrightarrow{g}}_{11})\\ ({\overleftarrow{g}}_{21},{\overrightarrow{g}}_{21})& ({\overleftarrow{g}}_{22},{\overrightarrow{g}}_{22})& ({\overleftarrow{g}}_{23},{\overrightarrow{g}}_{23})& ({\overleftarrow{g}}_{24},{\overrightarrow{g}}_{24})& ({\overleftarrow{g}}_{25},{\overrightarrow{g}}_{25})\\ ({\overleftarrow{g}}_{31},{\overrightarrow{g}}_{31})& ({\overleftarrow{g}}_{32},{\overrightarrow{g}}_{32})& ({\overleftarrow{g}}_{33},{\overrightarrow{g}}_{33})& ({\overleftarrow{g}}_{34},{\overrightarrow{g}}_{34})& ({\overleftarrow{g}}_{35},{\overrightarrow{g}}_{35})\\ \dots & \dots & \dots & \dots & \dots \\ ({\overleftarrow{g}}_{m1},{\overrightarrow{g}}_{m1})& ({\overleftarrow{g}}_{m2},{\overrightarrow{g}}_{m2})& ({\overleftarrow{g}}_{m3},{\overrightarrow{g}}_{m3})& ({\overleftarrow{g}}_{m4},{\overrightarrow{g}}_{m4})& ({\overleftarrow{g}}_{m5},{\overrightarrow{g}}_{m5})\end{array}\right)$$

(1)

Step 5. Calculate the evaluation result matrix. The evaluation result matrix is obtained by multiplying the weight $G$ matrix of the index set by the evaluation matrix. The evaluation results are represented as a matrix.

$$G=\left\{\left({\overleftarrow{g}}_1,{\overrightarrow{g}}_1\right),\left({\overleftarrow{g}}_2,{\overrightarrow{g}}_2\right),\dots ,\left({\overleftarrow{g}}_k,{\overrightarrow{g}}_k\right)\right\}$$

(2)

Here, $g_k$ ($k=1,2,3,4,5$) represents the evaluation results, $\leftarrow$ indicates that the development trend in the object is poor, and $\to$ indicates that the development trend in the object is excellent; the same variables apply below.

Step 6. Calculate the evaluation value. Since the model analyzes the evolution trend in objects $M\left(•,+\right)$ from the whole domain, and the weighted average fuzzy operator is compatible with all indicators, the operator is considered to calculate the evaluation value.

The development trend towards the negative evaluation value is

$$\overleftarrow{V}={\displaystyle \sum _{k=1}^5{\overleftarrow{g}}_k}{v}_k/{\displaystyle \sum _{k=1}^5{\overleftarrow{g}}_k}$$

(3)

The development trend towards the positive evaluation value is

$$\overrightarrow{V}={\displaystyle \sum _{k=1}^5{\overrightarrow{g}}_k}{v}_k/{\displaystyle \sum _{k=1}^5{\overrightarrow{g}}_k}$$

(4)

Here, $v_k$ is the score threshold of different evaluation grades, where ${\overleftarrow{g}}_k$ represents the evaluation results of the bad trend in different evaluation grades, and ${\overrightarrow{g}}_k$ represents the evaluation results of the good trend in different evaluation grades. If the $\overrightarrow{V}>\overleftarrow{V}$ evaluation value is $\overrightarrow{V}$, the evolution trend in the object is good; if the value is contrary to this, the trend is bad.

5. Evaluation Analysis

For each secondary index question in the collected questionnaire, screening and statistics were made according to the number of people filling in each evaluation level, and the weighted score value of each secondary index was calculated. At the same time, the membership vector was calculated.

Table 9. Dynamic membership degree of the secondary indicators.

Primary Indicator	Secondary Indicator	Dynamic Membership Degrees of Secondary Indicators
Cultural dimension (A1)	Conceptual Identification (A11)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.2},\overrightarrow{1.0})$, $(\overleftarrow{0.8},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Behavioral Cognition (A12)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.3},\overrightarrow{1.0})$, $(\overleftarrow{0.7},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Influence of Friends and Family (A13)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.8},\overrightarrow{1.0})$, $(\overleftarrow{0.2},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Cultural Environment (A14)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.6},\overrightarrow{1.0})$, $(\overleftarrow{0.4},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
Behavioral dimension (A2)	Public Transportation (A21)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.8},\overrightarrow{1.0})$, $(\overleftarrow{0.2},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Waste Sorting (A22)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.5},\overrightarrow{1.0})$, $(\overleftarrow{0.5},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Use of Disposable Products (A23)	$(\overleftarrow{0.1},\overrightarrow{1.0})$, $(\overleftarrow{0.9},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Clean Plate Campaign (A24)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.5},\overrightarrow{1.0})$, $(\overleftarrow{0.5},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
Technological dimension (A3)	Low Carbon Technology (A31)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.2},\overrightarrow{0.5})$, $(\overleftarrow{0.8},\overrightarrow{0.5})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Low Carbon Products (A32)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.4},\overrightarrow{1.0})$, $(\overleftarrow{0.6},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Garbage Harmless Treatment (A33)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.3},\overrightarrow{1.0})$, $(\overleftarrow{0.7},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Comprehensive Waste Utilization (A34)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.2},\overrightarrow{1.0})$, $(\overleftarrow{0.8},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
Environmental dimension (A4)	Air Quality (A41)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.7},\overrightarrow{1.0})$, $(\overleftarrow{0.3},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Sewage Treatment (A42)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.3},\overrightarrow{1.0})$, $(\overleftarrow{0.7},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Green Coverage (A43)	$(\overleftarrow{0.2},\overrightarrow{1.0})$, $(\overleftarrow{0.8},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$
	Solid Waste Disposal (A44)	$(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.6},\overrightarrow{1.0})$, $(\overleftarrow{0.4},\overrightarrow{1.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$, $(\overleftarrow{0.0},\overrightarrow{0.0})$

presents the dynamic membership degrees of each secondary indicator under different evaluation grades, which reflect the public’s behavioral characteristics under various indicators. The membership degree values range from 0 to 1. The higher the value, the stronger the degree of membership of that grade. For instance, from the membership degree of concept recognition $(\overleftarrow{0.8},\overrightarrow{1.0})$, it can be concluded that 80% of the public have a moderate understanding of the green and low-carbon concept. The membership degree of the relationship with relatives and friends indicates that 20% of the public believe that the influence of their relatives and friends on green and low-carbon behaviors is poor, suggesting that the positive driving effect of relatives and friends on the public’s low-carbon behaviors is limited. The low-carbon technology indicator shows that 80% of the public think that their current understanding of low-carbon technology is weak. Therefore, there is room for improvement in the understanding of the relationship with relatives and friends and low-carbon technology.

By multiplying the weights of each secondary indicator with their corresponding dynamic membership degrees, this study obtained the dynamic fuzzy evaluation result vector matrix for the secondary indicators. Subsequently, this study calculated the dynamic evaluation values for the secondary indicators using a weighted evaluation fuzzy operator. From the perspective of

Table 10. Dynamic evaluation value, grade, and trend in secondary indicators.

Secondary Indicator	Dynamic Evaluation Value	Grade	Trend
Conceptual Identification (A11)	82	good	Improving
Behavioral Cognition (A12)	83	good	Improving
Influence of Friends and Family (A13)	78	fair	Improving
Cultural Environment (A14)	86	good	Improving
Public Transportation (A21)	88	good	Improving
Waste Sorting (A22)	85	good	Improving
Use of Disposable Products (A23)	91	excellent	Improving
Clean Plate Campaign (A24)	85	good	Improving
Low carbon Technology (A31)	72	fair	Improving
Low Carbon Products (A32)	84	good	Improving
Garbage Harmless Treatment (A33)	83	good	Improving
Comprehensive Waste Utilization (A34)	82	good	Improving
Air Quality (A41)	87	good	Improving
Sewage Treatment (A42)	83	good	Improving
Green Coverage (A43)	92	excellent	Improving
Solid Waste Disposal (A44)	86	good	Improving

, all the indicators show a positive trend. The public demonstrates a significant positive upward trend in low-carbon behaviors. Among them, the usage of disposable goods and green coverage are at an excellent level, while the influence of relatives and friends and the understanding of low-carbon technology are at a general level, and the others are at a good level. This is consistent with the results in Table 9. The results also reflect that the driving effect of green behaviors among the social groups on the public is weak, and the public has a weak understanding of the application scenarios of low-carbon technologies, which requires active improvement.

The dynamic evaluation value of the primary indicators was calculated according to the dynamic fuzzy evaluation and analysis method. From

Table 11. Membership and dynamic evaluation value, grade, and trend.

Primary Indicator	Membership Degrees for Primary Indicators	Dynamic Evaluation Value	Grade	Trend
Cultural dimension (A1)	$(\overleftarrow{0.00},\overrightarrow{0.00})$, $(\overleftarrow{0.23},\overrightarrow{1.00})$, $(\overleftarrow{0.77},\overrightarrow{1.00})$, $(\overleftarrow{0.00},\overrightarrow{0.0}0)$, $(\overleftarrow{0.00},\overrightarrow{0.00})$	82.27	good	Improving
Behavioral dimension (A2)	$(\overleftarrow{0.00},\overrightarrow{0.00})$, $(\overleftarrow{0.77},\overrightarrow{1.00})$, $(\overleftarrow{0.24},\overrightarrow{1.00})$, $(\overleftarrow{0.00},\overrightarrow{0.0}0)$, $(\overleftarrow{0.00},\overrightarrow{0.00})$	87.65	good	Improving
Technological dimension (A3)	$(\overleftarrow{0.00},\overrightarrow{0.00})$, $(\overleftarrow{0.04},\overrightarrow{1.00})$, $(\overleftarrow{0.96},\overrightarrow{1.00})$, $(\overleftarrow{0.00},\overrightarrow{0.0}0)$, $(\overleftarrow{0.00},\overrightarrow{0.00})$	80.39	good	Improving
Environmental dimension (A4)	$(\overleftarrow{0.00},\overrightarrow{0.00})$, $(\overleftarrow{0.39},\overrightarrow{1.00})$, $(\overleftarrow{0.61},\overrightarrow{1.00})$, $(\overleftarrow{0.00},\overrightarrow{0.0}0)$, $(\overleftarrow{0.00},\overrightarrow{0.00})$	83.93	good	Improving

, the public’s membership degrees in the cultural dimension, behavioral dimension, technological dimension, and environmental dimension are all at a medium to high level. Meanwhile, the overall development direction is positive and promising. Among them, the evaluation value of the behavioral dimension is the highest, indicating that the public has a strong awareness and practice in specific low-carbon behaviors. The environmental dimension ranks second in the evaluation, suggesting that the public has a relatively high degree of recognition for environmental quality. However, the technological and cultural dimensions still have potential to be tapped.

The comprehensive evaluation value of green and low-carbon development awareness among the Chinese public, calculated using the dynamic fuzzy evaluation method, was $\overrightarrow{83.56}$. The results indicated that the current status quo of public awareness of green and low-carbon development is generally favorable, and the overall evolution trend is good.

6. Conclusions and Policy Implications

6.1. Conclusions

From the perspective of the secondary index of the evaluation elements, the influence of relatives and friends and low-carbon technology evaluation are at a general level, but it is worth noting that its development trend is positive. This shows that the public is greatly influenced by the dissemination of low-carbon perceptions by relatives and friends, and there is still room for improvement in low-carbon technologies. The evaluation of disposable commodity use and greening coverage is good, and the trend continues to improve, which reflects that the public has achieved good results in the use of disposable commodities and the emphasis on greening coverage, which needs to be maintained and consolidated in the future. The evaluation of other secondary indicators is better, and the trend is getting better, reflecting that the public has a relatively positive performance in green and low-carbon lifestyle.

From the perspective of the first-level indicators of evaluation elements, the overall evaluation values of the four dimensions—cultural identity, behavioral choices, technological support, and environmental construction—are all commendable, indicating that the transformation of the Chinese public’s green and low-carbon lifestyle is progressing in a multi-dimensional manner. In terms of cultural cognition, significant initial achievements have been made in both the public’s high level of recognition of low-carbon development concepts and the active construction of cultural environments. Regarding behavioral practice, there has been a substantial reduction in the use of disposable items. In the realm of technological application, low-carbon products have seen rapid widespread adoption. Concerning environmental construction, low-carbon infrastructure has reached a certain standard. These changes mark the transition from advocating concepts to deepening practical implementation, underscoring an urgent need for precise policy measures. It is essential to enhance capabilities for independent innovation in technology, narrow regional disparities in behavioral practice, and strengthen the balance of environmental governance, thereby facilitating the public’s transition from participants to leaders in the pursuit of a low-carbon lifestyle.

6.2. Policy Implications

Through a comprehensive analysis of the green and low-carbon lifestyle among the public, this study reveals the basic status quo, influencing factors, and development trend in the green and low-carbon lifestyle among the Chinese public. To consolidate the current positive development trend, based on the theories of social contagion and behavioral change, the following recommendations are proposed: adhere to the problem-oriented approach, focus on the relatively weak links in the evaluation elements such as ideological atmosphere and low-carbon technology, put forward measures to improve the coordination mechanism of all parties, improve the market incentive mechanism, and establish an education and publicity mechanism. Many studies have pointed out that external factors, such as infrastructure, publicity, education, and economic factors, can directly and indirectly encourage low-carbon behaviors [38]. We need creative, robust, and audacious strategies in governance, management, and education to catalyze mainstream sustainable development across scales and sectors [39].

(1)

Improve the coordination mechanism among all parties to form a joint force for green and low-carbon development.

First, the government should play a leading role in providing a sound system and directional guidance. Policymakers are suggested to enhance low-carbon behavior promotion through modifications to external factors [40]. Further improving policies and regulations, such as the already issued “Anti-Food Waste Law”, which restricts the dining behavior of the public from a legal perspective, promotes the “Clean Plate Campaign” to effectively reduce the phenomenon of food waste. The implementation of garbage classification management regulations in various places guides the public to correctly dispose of garbage, improves the recycling and utilization rate of resources, and alleviates pressure on the environment caused by garbage pollution. The government can also disseminate green and low-carbon knowledge to the public through subtle means, enhance the public awareness of low-carbon strategies, inspire the public to integrate low-carbon and energy-saving behaviors into their daily lives, and practice the concept of green and low-carbon lifestyles through various activities, such as National Low-Carbon Day.

Second, enterprises play a key role and provide a wide range of green and low-carbon products and services. In providing various green and low-carbon products and services, enterprises are an important force promoting a green and low-carbon lifestyle. Take the shared bicycle business launched by Hellobike Group as an example; this service has settled in more than 500 cities and brought together 600 million users to weaken the dependence on private cars, reduce carbon emissions in the transportation sector, and provide citizens with a convenient and low-emission short-distance travel option. In addition, many enterprises incorporate low-carbon goals into the product design and manufacturing process through various means, such as clean energy production, production process optimization, and the selection of environmentally friendly materials to reduce carbon emissions from the source as well as to provide positive guidance for citizens to practice a green and low-carbon lifestyle. Green technological innovation is a long-term, uncertain, complex, and high-risk strategic behavior for enterprises, with disruptive green technological innovation exhibiting even stronger externalities [41].

Third, it is recommended to give full play to the role of social organizations and advocate a green and low-carbon living atmosphere. Social organizations can promote the public’s understanding of and enthusiasm for a green and low-carbon lifestyle through various public welfare activities, such as planning low-carbon publicity and conducting environmental protection practices. Some social organizations also invite environmental protection experts to conduct green and low-carbon lectures in communities, so that residents’ understanding of low-carbon lifestyle continues to improve. Some social organizations also encourage residents to exchange idle items to promote the dynamic cycle of idle resources. In addition, the creation of low-carbon communities and low-carbon consumption districts is also very important. It can not only attract consumers to practice the concept of low-carbon consumption but also promote the integration of surrounding residents into a green and low-carbon lifestyle and provide reference experiences for other regions.

(2)

Improve the market incentive mechanism and stimulate the driving force for green and low-carbon development.

First, the incentive mechanism for green consumption can be improved. On the one hand, economic factors are believed to exert a significant influence on individuals’ low-carbon lifestyle based on personal cost–benefit considerations [42]. Policies such as tax reductions and financial subsidies can be used to encourage consumers to purchase green products. For example, offering discounts and tax incentives for the purchase of new energy vehicles or energy-efficient home appliances can reduce the purchase cost for consumers while enhancing the competitiveness of green products and tilting the choice towards more energy-efficient options. This preference can invisibly boost the upgrading of the green industry and the transformation of consumption patterns. On the other hand, the construction of a system for the recycling and utilization of waste materials can be accelerated, and the recycling and reuse of waste household appliances, automobiles, and electronic products can be further promoted. Conducting trade-ins and old item recycling services can not only alleviate the environmental pressure caused by resource waste but also provide tangible benefits to consumers, thereby stimulating their enthusiasm for practicing green and low-carbon lifestyles. Moreover, enterprises need to focus on enhancing their technological innovation capabilities and launching more high-quality green and healthy products to improve the quality level of the green product supply. Only in this way can the public’s green consumption demands receive better feedback and further catalyze the deeper integration of low-carbon and environmentally friendly living patterns into daily life.

Second, the carbon trading market can be improved. The construction of a carbon trading market is of great significance in reducing greenhouse gas emissions through market mechanisms. As an innovative environmental regulation tool, carbon emission trading policies inject new vitality into urban green technological innovation by setting emission caps and introducing market mechanisms [43]. Currently, China’s carbon trading market is in a stage of continuous development and improvement, and the construction of a carbon emission quota allocation mechanism is a top priority. The scientific allocation of carbon emission quotas should take into account the actual carbon emissions and emission reduction potential of different industries and enterprises, so as to ensure fairness and encourage enterprises to take the initiative to reduce emissions and lower carbon levels. Additionally, the intensity of supervision needs to be strengthened in the carbon trading market. To ensure the transparent and fair market operations, market manipulation, illegal transactions, and other behaviors need to be effectively prevented, and the relevant trading rules and information disclosure systems of the carbon trading market need to be further improved. The level of public awareness and participation in carbon trading also need to be continuously enhanced. By establishing personal carbon accounts and conducting pilot projects of personal carbon trading, the public can be encouraged to practice a green and low-carbon lifestyle and help society to achieve carbon reduction goals. This enables the public to personally participate in carbon trading and convert the carbon emission reduction achieved through their own low-carbon behaviors into actual economic value.

Third, innovative green financial policies should be developed. To ensure the achievement of carbon control goals, local governments should provide effective safeguards for the external environment of technological innovation through improving institutional arrangements, offering diversified green financial policies, and fostering a culture that values green development [44,45,46]. Green finance provides financial support for green and low-carbon projects. The development of green finance will facilitate technological innovation, improve resource utilization efficiency, and promote industrial upgrading [47,48,49]. Green finance, as an important support for driving the low-carbon transformation, can provide solid financial guarantees for green development through the allocation of diversified financial instruments. On the one hand, financial institutions should take the initiative to develop a variety of green financial products, such as innovative products like green credit, green bonds, and carbon financial derivatives, and build a financing service system that covers the entire life cycle of green and low-carbon projects, effectively reducing the financing difficulties of green and low-carbon development. On the other hand, the guiding role of policies should be promoted, encouraging the establishment of green investment funds, attracting more social capital to enter the field of green and low-carbon development, and providing stable financial support for green and low-carbon projects. At the same time, technologies such as big data and artificial intelligence should be fully utilized to build an intelligent risk assessment platform, accurately identify potential risks, and improve the security and effectiveness of capital allocation.

(3)

Establish an education and publicity mechanism to enhance the attractiveness of green and low-carbon development.

On the one hand, an education system for green and low-carbon development should be established. From the early education stage, students’ environmental awareness and low-carbon lifestyle habits should be cultivated, and the concept of a green and low-carbon lifestyle should be fully integrated into the education system. The “Implementation Plan for the Construction of a Green and Low-Carbon Development Education System” issued by the Ministry of Education emphasizes this point by incorporating the basic concepts and knowledge of the carbon peak and carbon neutrality into the teaching of subjects such as biology, geography, and physics. In the early education stage, children’s green and low-carbon awareness can be cultivated through picture books and games. In the basic education stage, the basic concepts and knowledge of the carbon peak and carbon neutrality should be popularized in the teaching of these subjects. In higher education, multi-disciplinary integration should be strengthened, and carbon peak and carbon neutrality centers and core knowledge systems should be established, covering multiple cross-disciplinary fields.

On the other hand, new media platforms such as short videos should be fully utilized for popular science publicity on the green and low-carbon lifestyle. Short videos have become an effective carrier for promoting a low-carbon lifestyle. Their concise and easy-to-understand characteristics have been deeply rooted in the public’s minds. Relevant departments or environmental protection organizations can design a series of vivid and easy-to-understand short videos on a low-carbon lifestyle, covering topics such as low-carbon travel, energy conservation, water conservation, and garbage classification. For example, the details of garbage classification can be presented through animations, or the reduction in carbon emissions by public transportation compared to private cars can be demonstrated through vivid cases. These videos can be widely promoted through new media platforms such as TikTok, Kuaishou, and Weibo, allowing the public to accept the popularization of a green and low-carbon lifestyle in a relaxed and pleasant way.

Furthermore, conducting specialized education in communities is also an effective method of publicity. Social, family, and educational factors have significant impacts on students’ low-carbon education [50]. Education plays a crucial role in promoting green development by shaping environmentally friendly production behaviors and fostering a low-carbon lifestyle [51]. In community life, targeted lectures should be held based on the age and living conditions of residents. Some examples include sharing tips on the efficient use of household appliances, selection of low-carbon foods, and cooking healthy meals, as well as reducing the use of disposable products or bringing residents’ own water bottles and other practical tips related to daily life. In addition, residents can be organized to share their experiences and practical knowledge about the low-carbon lifestyle, enhancing their sense of participation and recognition.

6.3. Limitations and Future Research

This paper focuses on the topic of the green and low-carbon lifestyle among the public and conducts both breadth and depth research. Some achievements have been made in the description of the current situation, the analysis of the influencing factors, and the construction of the promoting strategies, but there are still some shortcomings.

First, there are restrictive factors in the study sample. Although this study strived for diversity in the sample selection, the sample was mainly composed of people aged 19 to 45. This group has similarities in cognitive levels, lifestyles, and socioeconomic status. This may lead to an overestimation of the representativeness of the green and low-carbon lifestyle of this age group in the research results. Meanwhile, it may insufficiently reflect the situations of people in other age groups, making it impossible to accurately present the differences and the full picture of the green and low-carbon lifestyles of the public in different age groups.

Second, the analysis of influencing factors is slightly weak. This paper’s exploration of the deep-level interaction mechanism is rather superficial. In the process of the formation of green and low-carbon lifestyles, factors such as individual characteristics, socioeconomic backgrounds, environmental awareness, and policies and regulations are intertwined. However, this paper fails to deeply reveal the internal logic of the mutual influence among these factors. This may lead to misjudgments about the influence of each factor, making the proposed promotion strategies lack pertinence and effectiveness.

Third, there was a lack of attention to emerging areas. With the development of the times, emerging fields such as current digital technologies and new consumption patterns are having a profound impact on green and low-carbon lifestyles. However, this research has barely touched upon these areas. This may result in an incomplete assessment of the current situation of the public’s green and low-carbon lifestyle, and an underestimation of the public’s achievements in green and low-carbon practices and their development potential.

Fourth, the potential impact of social desirability bias on the data has not been fully discussed. When answering questions related to attitudes and behaviors regarding ecology and climate, respondents may be inclined to present an image that conforms to social norms, resulting in social desirability bias, which may cause their self-reported low-carbon behaviors or environmental protection intentions to be higher than the actual situation.

In view of the above shortcomings, future studies can be deepened and expanded in the following respects:

First, studies of group differences need to be further refined. Future studies should rely on large-scale representative sample surveys, supplemented by in-depth interviews, and in-depth explorations of the characteristics of various groups from multiple perspectives to identify the specific differences of people from different regions, ages, occupations, and cultural backgrounds, so as to provide support for the targeted formulation of precise strategies.

Second, multi-factor interaction research needs to be deepened. By introducing complex research methods, such as system analysis, structural equation modeling, etc., we can deeply consider the complex relationships between individual, social, policy, and other factors, and build a comprehensive action model of influencing factors, so as to analyze the internal influence mechanism of promoting or hindering a green and low-carbon lifestyle.

Furthermore, to further broaden the research horizon. Future research should focus on new opportunities and challenges brought about by emerging fields. For example, the model and development potential of applying digital technology in promoting a green and low-carbon lifestyle need to be deeply explored.

Finally, the impact of social desirability bias can be optimized. Future research can be optimized in three respects. First, lie-detection or reverse-verification questions can be set to identify social desirability bias. Second, objective data such as consumption and travel records can be combined to verify the authenticity of self-reports. Third, implicit association tests or scenario-simulation experiments can be used to reduce the interference of social norms and obtain unconscious attitudes. This can make the data more reliable and reveal the problems in green and low-carbon behaviors more accurately.