Li Liu
Kaiqing Wang*- School of Economics and Management, China University of Petroleum (Beijing), Beijing, China
Although achieving China's “3060” dual-carbon goals is an urgent priority, current research in the field of carbon labeling has paid limited attention to its pathways on consumers' green purchase decision. Drawing on market signaling theory, this paper proposes an innovative purchase decision model for carbon-labeling items. First, based on the theory of action (TRA), we have proposed the hypothesis that subjective norms and attitudes positively influence carbon labeling purchase intentions. Second, we initially extended TRA by incorporating two variables: environmental values and low-carbon knowledge. Finally, we introduce the quality trust variable, offering a completely new viewpoint to extend the TRA model. We adopted a structural equation model (SEM) approach to examine our research model, using 341 samples. The results reveal that consumers' decisions to purchase carbon-labeling items are primarily influenced by their trust in items' quality and attitudes. Additionally, low-carbon knowledge and environmental values directly or indirectly affect purchasing intentions, while subjective norms also demonstrate a positive impact on decision-making. By extending TRA, this study clarifies the decision-making process behind carbon-labeling items consumption, offering significant insights for advancing carbon labeling systems and fostering green consumption trends.
1 Introduction
In the face of increasingly stringent carbon emission reduction targets, carbon labeling—a key metric that quantifies the carbon footprint of items throughout their life cycle—has attracted global attention (Zhou et al., 2019). Developed countries such as the UK, the US, Canada and France have introduced carbon labeling systems to create green trade barriers, such as tariff barriers. These systems require exported products or services to display their carbon footprints (Zhao et al., 2018). To obtain carbon labels and enhance their green image and market competitiveness, companies must assess the life cycle carbon footprints of their products or services (Vandenbergh et al., 2011). China's path toward carbon labeling is further complicated by its unique national circumstances, which differ fundamentally from the experiences of developed nations. These characteristics include its immense scale, rapid industrialization, and a government-led model of socioeconomic development. Consequently, while international systems offer valuable references, they cannot fully capture the socioeconomic realities and cultural nuances that shape consumer behavior in China (Ding et al., 2024).
Historically, governments and businesses have been the main drivers of reducing carbon emissions. Governments have used environmental policies to reduce fossil fuel consumption and encourage industrial upgrades. However, the robust energy demand resulting from industrialization and urbanization may create conflicts between mandatory emission reductions and economic growth. Correspondingly, China's carbon labeling system is still in its nascent stages, with public awareness remaining limited and the interplay between consumer choices, corporate interests, and national environmental goals still being navigated. This context makes the study of individual green consumption behavior particularly critical and timely (Ding et al., 2018). In recent years, as environmental issues have become more pressing, public awareness of environmental protection has increased, as has interest in low-carbon lifestyles. Carbon labeling systems can reduce the imbalance of information between businesses and consumers, encouraging governments, companies and consumers to participate together in low-carbon economic development (Xu and Boqiang, 2021).
Our research specifically targets the unique cultural context of China, where traditional values, modern consumerism, and state-led environmental initiatives converge to influence consumer decisions. Understanding how these specific cultural and social drivers affect consumer perception and response to carbon labels is essential for designing a system that is not only effective but also genuinely embraced by the Chinese populace. Accordingly, this study uses a questionnaire survey to examine the factors and mechanisms influencing consumers' decisions to purchase items with carbon labeling, focusing on informed consumers. The aim is to provide a theoretical basis and policy recommendations for China's carbon labeling system. The remainder of this paper is structured as follows: Section 2 reviews the literature on carbon-labeling consumption and proposes a purchase decision model based on prior findings and hypotheses; Section 3 describes the research methodology, data sources, validity and reliability tests; Section 4 presents the survey data analysis and results; Section 5 discusses theoretical implications, practical implications, limitations and future research directions.
2 Literature review
2.1 Carbon labeling
An item's carbon footprint refers to the total greenhouse gas emissions generated throughout its life cycle, from cradle to grave. As an environmental management tool, carbon labeling evaluates and displays the carbon footprint (measured in CO2 equivalents) of consumer products, providing reference points for purchasing decisions (Liu et al., 2015). Since the UK piloted the world's first carbon-labeled products in March 2007, developed nations like the US and EU have followed suit (Zhang et al., 2017). Consequently, most carbon labeling research has focused on these countries. For example, in Germany, Emberger-Klein and Klaus (2018) concluded that carbon labeling effectively reduces greenhouse gas emissions in supermarket food choices by directly influencing consumer behavior. In the United States, research by Avon et al. (2024) found that carbon footprint labels are more effective at enhancing perceptions of sustainability and purchase intent for animal-based products.
Given that developing countries are major sources of energy consumption and emissions, implementing carbon labeling systems is particularly critical. Although research in these regions remains limited, some scholars have begun exploring the topic. For instance, Sureeyatanapas et al. (2021) investigated factors driving Thailand's carbon footprint program and potential long-term barriers. Mostafa (2016) examined Egyptian consumers' willingness to pay for carbon-labeled products. As China's carbon labeling system develops, domestic scholars have also turned their attention to this field. Zhao et al. (2021) found that public awareness of carbon labeling in China remains low and systematically analyzed its policy implications for carbon neutrality. Chen et al. (2024) assessed urban consumers' willingness to pay for carbon-neutral labeled beef products and estimated associated greenhouse gas emissions. Yang and Lin (2024) examined the dynamic changes in Chinese consumers' willingness to pay for different types of carbon-labeled products. Yuan and Tang (2025) reveal that carbon labels are effective in encouraging consumers to choose low-emission conventional meat products, especially when using traffic-light carbon label.
Literature reviews indicate that existing research primarily focuses on carbon label design (Carrero et al., 2021; Holenweger et al., 2023; Panzone et al., 2021), willingness to pay (WTP) premiums for labeled products (Zhao et al., 2018; Li et al., 2017; Zhao et al., 2020; Lin et al., 2022), international comparisons of labeling systems, and their trade impacts (Liu et al., 2015; Zhang et al., 2017; Shuai and Zhang, 2013). Additionally, some scholars have highlighted the potential pitfalls of carbon labeling as a tool, such as Avon et al. (2024) directly address the risk of greenwashing associated with carbon labeling for meat products and Sureeyatanapas et al. (2021) discuss the effectiveness of carbon labeling in actually altering consumer behavior in the real world. Past studies have rarely analyzed consumer purchasing decisions, thus failing to examine the causal pathways from stimulus to response. This study addresses this gap by focusing on Chinese university students—a demographic that will play a pivotal role in achieving the “3060” goals. As future decision-makers and leaders from 2030 to 2060, this group is shaping consumption preferences and habits through product choices (Chen and Zheng, 2012; Xiong et al., 2014). Although pioneering scholars have studied the impact of carbon labeling on the consumption of agricultural products (Song et al., 2024), research targeting Chinese university students remains unexplored. This study not only fills this gap but also provides valuable insights for China's dual-carbon objectives.
2.2 Hypotheses development
Prior research has widely applied the Theory of Reasoned Action (TRA) to model green purchasing behavior across contexts, including energy-efficient products (Tan et al., 2017), recycling (Wang et al., 2016), general green consumption (Kautish et al., 2019), and sustainable apparel (Rausch et al., 2021). The classic TRA model treats subjective norms and attitudes as core variables, analyzing their effects on behavioral intentions to explain consumer actions (McCarthy et al., 2003). Subjective norms refer to perceived social pressures to conform to expectations (Krumpal, 2013). Empirical studies confirm that subjective norms significantly influenced both personal norms and intentions (Maleknia et al., 2025). Attitudes, defined as evaluative judgments toward specific objects or behaviors (Cooper and Croyle, 1984), play an equally critical role. Low-carbon attitudes—shaped by individual values and contextual factors (Bai and Liu, 2013)—exhibit strong associations with energy-saving behaviors (Gadenne et al., 2011). At the same time, consumer attitudes are one of the key drivers behind purchasing carbon-labeled products (Sun et al., 2023). Purchase intention, conceptualized as a planned precursor to action, reflects the likelihood of translating attitudes into product-specific behaviors, constrained by subjective norms (Singh and Priyanka, 2017; Paco and Tania, 2017). Building on this theoretical foundation, we propose the following hypotheses:
H1: There is a positive relationship between subjective norms and purchase intentions for carbon-labeling items.
H2: There is a positive relationship between low-carbon attitudes and carbon-labeling purchase intentions.
The Value-Belief-Norm (VBN) theory of environmentalism is an emerging framework for understanding consumer pro-environmental behavior, positing that self-transcendent values (altruistic/biospheric)—as opposed to self-enhancement (egoistic) values—strongly predict pro-environmental actions (Özekici, 2022). However, egoistic value measurements may lack validity in collectivistic societies, where collectivistic values (prioritizing cooperation, group goals, and helping others) are more salient predictors (Wang et al., 2020). This is evident in East Asian societies (e.g., China, Japan, Korea): collectivistic values correlate positively with pro-environmental behavior (Kim and Choi, 2005), Chinese consumers (collectivistic/nature-oriented) show stronger green tendencies (Coleman et al., 2011), and collectivistic vs. individualistic (e.g., American) consumers differ significantly in environmental attitudes, knowledge, norms, and intentions—with collectivistic values explaining substantial variance in pro-environmental attitudes/intentions (Chen, 2013). Thus, Existing research targeting Chinese consumers rarely differentiates self-transcendent from self-enhancement values, with studies like Gu combining them to confirm their positive influence (Gu, 2022). Additionally, Wang et al. (2023) research on Chinese consumers‘ green purchase intention to visit green hotels shows that collectivistic values have a positive impact on attitudes. Bai and Liu (2013) identified low-carbon knowledge indirectly promotes green behavior via environmental values, which in turn shape attitudes. Low carbon knowledge indicates people's understanding of the importance of a low-carbon lifestyle (Steg, 2008). The research findings of Lin and Yang indicate that low-carbon knowledge can promote environmental awareness (Lin and Yang, 2022). Moreover, economic and knowledge gaps may hinder or suppress individuals' environmental values and low-carbon behaviors (Salmela and Vilja, 2006). Therefore, this study proposes the following reasonable hypotheses:
H3: There is a positive relationship between environmental values and low-carbon attitudes.
H4: There is a positive relationship between low-carbon knowledge and environmental values.
Inspired by market signaling theory, this study incorporates the dimension of quality trust to expand the existing model of green consumption influence. Trust is regarded as an expectation that impacts public behavioral decisions regarding the future (Kramer, 1999). It is also a key factor in enhancing consumer acceptance of low-carbon products (Gu et al., 2025). Particularly in many developing countries like China, trust plays a crucial role in influencing people's choices of action. In the past, due to severe information asymmetry, consumers lacked a clear understanding of the specific details of green products (Yang, 2015). Carbon labeling, as a market signal, communicates the environmental attributes of products to consumers. More comprehensive information helps to mitigate the bullwhip effect (He et al., 2015) and can influence individuals' perceptions of product attributes. For instance, compared to buildings labeled as “high carbon emission,” people tend to perceive higher comfort and satisfaction with the indoor environment of buildings labeled as “low carbon emission,” even though the actual environmental conditions of the two may not differ significantly (Holmgren et al., 2017); even with identical light sources, participants felt more comfortable under environmentally friendly labeled desk lamps and demonstrated better color discrimination abilities in tests (Sörqvist et al., 2015); a study selected 10 buildings with similar characteristics, differing only in green certification, and conducted a 5-day experiment on their occupants. The results showed that residents in green-certified buildings scored higher on cognitive tests, had better sleep quality, higher environmental satisfaction, and fewer symptoms of sick building syndrome, indicating that the certification effect extends beyond physical conditions (MacNaughton et al., 2017).
In recent years, numerous scholars have begun to explore the underlying reasons behind the trust effect of environmental labels. Zauskova's research confirmed the positive impact of trust on eco-product labels and shopping decisions (Zauskova et al., 2013). Pahlevi and Dwi (2020) integrated green quality trust and green perceived value into the quality-loyalty model to predict loyalty to environmentally friendly plastic products. Zou et al. (2024) conducted an empirical study on the factors influencing the willingness to purchase used electric vehicles in China and found that functional risk and perceived risk have direct or indirect negative effects on brand trust and purchase intention. Saeed et al. (2025) empirically tested the positive correlation between individual green values and green brand quality trust, green loyalty, and green purchase intention. The most relevant research to this paper comes from Roh et al.'s expansion of the TRA model. By adding trust and perceived knowledge variables to the classic TRA model, they empirically demonstrated that green perceived value has a significant positive impact on trust in organic food, trust has a significant positive impact on purchase intention for organic food, and perceived knowledge has a significant positive impact on trust in organic food (Roh et al., 2022). Therefore, this study proposes the following reasonable hypotheses:
H5: There is a positive relationship between trust in carbon-labeling items' quality and purchase intentions.
H6: There is a positive relationship between environmental values and quality trust for carbon-labeling items.
H7: There is a positive relationship between low-carbon knowledge and quality trust for carbon-labeling items.
The Research model is illustrated in Figure 1.
Figure 1. Research model.
3 Data a methodology
3.1 Measurements and analysis methods
The variables in this study were measured using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). For the measurement of Environmental Values (EV), we adopted Bai and Liu's two items (capturing biospheric and anthropocentric values) as Questions 1 and 4 of our scale. Following Mi et al.'s argument that environmental values are the key catalyst for fostering public responsibility and further promoting pro-environmental behavior, we selected their four environmental value scale items as Questions 2, 3, 5, and 6 (Bai and Liu, 2013; Mi et al., 2016). Questions 7–9, measuring Low-carbon Knowledge (LK), and questions 14–16, measuring Attitude (ATT), were revised from the scale developed by Bai and Liu (Bai and Liu, 2013). Low-carbon Knowledge is defined as individuals‘ knowledge of facts or concepts related to low-carbon issues. Attitude refers to the degree to which an individual holds a favorable or unfavorable evaluation of a specific behavior. Specifically, we selected low-carbon items and rewrote the original questions to focus on carbon-labeling issues. Questions 10–13, measuring Subjective Norm (SN), questions 17–20, measuring Quality Trust (QT), and questions 21–25, measuring Purchase Intention (PI), were revised from the scale developed by Roh et al. (2022). Subjective Norm refers to the social pressure to act in a certain way. We developed and measured four questionnaires that asked about family, friends, the government and society in relation to carbon-labeling products. Quality Trust, which is defined as consumers' awareness that a specific product is helpful or at least harmless, was developed and validated using four items that addressed personal physical health, product lifespan, brands and sensory perception. Finally, Purchase Intention—characterized by a consumers' willingness to acquire a specific product—was evaluated using five questions that address preferences for carbon-labeling and relevant considerations (Roh et al., 2022). Given that knowledge of carbon labeling is not yet widespread in China, this study provided a brief introduction to carbon footprints and carbon labels after surveying the respondents' familiarity with carbon labels, to help them better understand the definition and significance of carbon labels. The content and images for the introduction to carbon labels in China were sourced from Baidu Encyclopedia entries.
The data analysis method employed Structural Equation Modeling (SEM) using SPSS 27.0 and SPSS AMOS 29.0. SEM is a comprehensive technique that integrates multiple regression, factor analysis, and path analysis, aiming to explore the most concise and precise relationships between variables by analyzing observed data. SEM comprises two main parts: the measurement model and the structural model. The measurement model focuses on validating the relationships between observed variables and latent variables by combining regression and factor analysis techniques to assess factor loadings and adjust or exclude observed variables with low loadings, thereby improving model fit. The structural model, on the other hand, is dedicated to examining the interactions between endogenous latent variables and exogenous latent variables, utilizing regression and path analysis to calculate regression coefficients and reveal direct effects between variables. SEM is suitable for investigating complex relationships between variables that are difficult to measure directly and has been widely used in empirical research in recent years.
Scale validation was conducted through both a pilot survey and a formal survey. The pilot survey was conducted on December 20, 2024, in a digital marketing class at China University of Petroleum (Beijing), using a paper-based questionnaire that was filled out on-site and then collected and entered. SPSS 27.0 was used to conduct reliability and validity tests. Based on the requirement that each factor's Cronbach's α coefficient should not be lower than 0.6, we tested the reliability of each subscale. Subsequently, we assessed the convergent and discriminant validity of the scale through exploratory factor analysis and revised and eliminated inappropriate items from the initially constructed scale. Ultimately, the final formal scale included 25 items. The specific content of the scale is presented in Appendix questionnaire.
3.2 Research context and samples
Our preliminary survey of consumers at mainstream Chinese universities revealed that fewer than 30% had heard of carbon labeling, with affirmative responses primarily coming from institutions with energy-related specialisms. This led us to believe that general consumers might be confused about carbon labeling. This uncertainty could compromise the authenticity of subsequent measurements across related dimensions. Consequently, we decided to focus our research on informed early adopters, selecting students from the China University of Petroleum (Beijing) as our sample cohort. Given that China University of Petroleum (Beijing) is a renowned institution specializing in the energy sector, its students generally possess a strong academic background in energy-related disciplines. This implies that these students are exposed to more comprehensive and in-depth knowledge about carbon neutrality, energy conservation, and sustainable development in their daily studies and research. Furthermore, as they are in the prime of their learning years, they exhibit higher sensitivity and receptiveness to emerging concepts and technologies related to carbon neutrality. As they become a major consuming force with their economic power, their influence will coincide with the critical stage of China's “dual carbon” goals (i.e., peak carbon dioxide emissions and carbon neutrality). During this pivotal historical period, they are not only witnesses to the development of carbon-neutral technologies and policies but also the backbone of future efforts to drive China toward achieving its dual carbon objectives. In view of this, the formal survey was conducted on March 6, 2025, through the Wenjuanxing platform, led by faculty and instructors from China University of Petroleum (Beijing) who distributed the questionnaire in course and grade WeChat groups. To prevent duplicate responses, the questionnaire system was set to allow only one response per device, and participants received a small red packet as compensation. A potential limitation of this study is that all researchers and respondents belonged to the same university, which may introduce social desirability bias (e.g., respondents exaggerating their support for low-carbon products to align with academic expectations). To mitigate this risk, two measures were implemented: Firstly, Respondents were explicitly informed that their answers would be strictly anonymous, used solely for academic research, and would not be linked to personal information; Secondly, The survey was administered online via a third-party platform (Wenjuanxing), ensuring no direct contact between researchers and respondents. The questionnaire was open for 1 week, and a total of 365 responses were collected. After excluding 24 invalid responses, 341 valid responses were obtained, meeting the statistical analysis principle that the number of participants should be 3 to 5 times the number of observed variables in the scale. Besides, N = 341 is adequate for the number of parameters (341/25 = 13.6:1 ratio).
The sample characteristic distribution is described in Table 1. The majority of respondents are aged between 18 and 23 (N = 295, 86.5%), which aligns with our assumption of targeting a young student population. The sample includes 168 males (49.3%) and 173 females (50.7%) with a balanced gender distribution. Among the respondents, 182 (53.4%) have an urban household registration, and 159 (46.6%) have a rural household registration. Their hometown living environments are evenly distributed across different levels of economic development, each accounting for 5–25%, without any particular bias. The most common monthly consumption expenditure of the respondents is between 1,500 and 3,000 RMB (N = 215, 63%), and the majority of respondents primarily shop online (N = 299, 87.7%). Two hundred and forty-seven respondents (72.4%) have heard of carbon footprint, while only 156 respondents (45.7%) have heard of carbon labels.
Table 1. Sample demographic.
3.3 Validity and reliability tests
This study uses self-reported scales that are susceptible to environmental factors, the emotions and cognitive states of the participants, and their response tendencies. These factors may introduce common method bias, which could affect the results. Therefore, the bias was tested prior to hypothesis testing using Harman's single-factor test, following the method of Eby L. T. and Dobbins G. H. As shown in Table 2, unrotated factor analysis of all items extracted six factors with Characteristic roots >1. The first common factor explained only 38.4% of the total variance (below the empirical threshold of 40%), indicating that there was no common method bias (Eby and Dobbins, 1997).
Table 2. Unrotated factor analysis results.
In terms of validity, on one hand, Exploratory Factor Analysis (EFA) was conducted using SPSS 27.0 software to assess the convergent and discriminant validity of the scale (Ruscio and Brendan, 2012). Before performing factor analysis, the prerequisites were met through the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity (Izquierdo et al., 2014). In this study, the KMO value for the 25 questionnaire items was 0.908, exceeding the acceptable standard of 0.7. Additionally, Bartlett's test of sphericity was significant (p = 0.000), indicating that the data were suitable for factor analysis. Principal component analysis further categorized the measurement indicators of the latent variables into 6 factors, which collectively explained 72.92% of the variance. After applying Kaiser normalization and varimax orthogonal rotation, all measurement indicators demonstrated factor loadings exceeding 0.5 on their corresponding latent variables, indicating good convergent validity of the scale; simultaneously, these measurement indicators showed factor loadings below 0.5 on other latent variables, suggesting that the discriminant validity of the scale was also satisfactory.
On the other hand, Confirmatory Factor Analysis (CFA) was employed to examine the validity of the scale (Mi et al., 2016). First, the standardized factor loadings on each latent variable were all >0.5, with t-values >1.96, as shown in Table 3. According to the criteria proposed by Kline (Kline, 1998), if the absolute value of skewness is within 3 and the absolute value of kurtosis is within 8, the data can be considered to meet the requirements of approximate normal distribution. As shown in the analysis results of Table 3, the absolute values of skewness and kurtosis coefficients for each measurement item in this study were within the standard range. Therefore, it can be concluded that all measurement items satisfy the condition of approximate normal distribution.
Table 3. Normality Tests and standardized factor loading.
Confirmatory Factor Analysis (CFA) was conducted using SPSS AMOS 29.0 on all measurement indicators of the variables to examine the Average Variance Extracted (AVE) and Composite Reliability (CR) for each dimension of the scale. The testing process involved establishing the standardized factor loadings of each measurement item on its corresponding dimension and then calculating the AVE and CR values for each dimension using their respective formulas. As shown in Table 4, the AVE values for each dimension exceeded 0.5, and the CR values exceeded 0.7, indicating that the data collected from the questionnaire in this study demonstrated good convergent validity and composite reliability. Furthermore, in the discriminant validity test, the standardized correlation coefficients between each pair of dimensions were smaller than the square root of the AVE value corresponding to each dimension, as shown in Table 4. This indicates that each dimension exhibited good discriminant validity.
Table 4. Validity and reliability tests.
In terms of reliability, SPSS 27.0 was used to test the reliability of the latent variable scale data for each dimension. The results showed that the Cronbach's α coefficients for all variables were greater than the standard value of 0.7, as presented in Table 3. This indicates that the scales used in this study demonstrated good internal consistency and excellent reliability.
3.4 Correlation analysis
In this analysis, Pearson correlation analysis was employed to explore the relationships between each of the variables. According to the results presented in Table 5, all variables in this analysis were found to have significant correlations, all at a 99% confidence level. Based on the correlation coefficients, all values were greater than 0, collectively indicating that in this analysis, all variables exhibited significant positive correlations with one another.
Table 5. Correlation testing.
4 Empirical results
4.1 SEM model fit assessment
Based on the model fit test results presented in Table 6, it can be observed that the CMIN/DF (Chi-square to degrees of freedom ratio) is 2.807, which falls within the acceptable range of 1–3. The RMSEA (Root Mean Square Error of Approximation) is 0.073, which is within the acceptable range of less than 0.08. Additionally, the test results for IFI, TLI, and CFI all reached above the acceptable level of 0.8. Therefore, considering the results of this analysis, it can be concluded that the model demonstrates good fit.
Table 6. Model fit indices.
4.2 Path relationship test results
Figure 2 clearly illustrates the standardized path coefficients and their significance test results among the latent variables in this study's model. Notably, all path coefficients indicate a positive influence, and all are significant at the 0.001 statistical significance level. The analytical results presented in Table 7 provide a detailed demonstration of the theoretical model testing across three stages in this study, strongly supporting all hypotheses. Our results indicate that subjective norms have a significant positive effect on purchase intention (β = 0.253, p < 0.001). This corresponds with some previous studies have shown that subjective norms significantly influenced both personal norms and intentions (Bai and Liu, 2013). Thus, H1 is accepted. Concurrently, consumers' low-carbon attitude also significantly enhances purchase intentions (β = 0.354, p < 0.001).
Figure 2. Results of SEM. (1) Path coeffcients are standardized, (2) *p < 0.05, **p < 0.01, ***p < 0.001.
Table 7. Path coefficient test results.
Our finding corresponds to certain empirical studies that showed consumer attitudes are one of the key drivers behind purchasing carbon-labeled products (Sun et al., 2023). Therefore, H2 is supported. Empirical analysis reveals that environmental values significantly strengthen consumers' low-carbon attitude (β = 0.639, p < 0.001). This corresponds with the findings of some researchers who argue that collectivistic values have a positive impact on attitudes (Wang et al., 2023). Thus, H3 is accepted. Meanwhile, low-carbon knowledge significantly elevates consumers' environmental values (β = 0.299, p < 0.001). It corresponds to certain empirical studies that showed ow-carbon knowledge can promote environmental awareness (Lin and Yang, 2022). Thus, H4 is accepted. The findings show that consumers' quality trust in carbon-labeling items has a very strong positive impact on their purchase intention (β = 0.511, p < 0.001). Trust is a key factor in enhancing consumer acceptance of low-carbon products (Gu et al., 2025). Therefore, H5 is supported. Furthermore, environmental values (β = 0.295, p < 0.001) and low-carbon knowledge (β = 0.506, p < 0.001) both significantly enhance consumers' quality trust in carbon-labeling items. Those findings correspond to certain empirical studies that showed green perceived value and perceived knowledge has a significant positive impact on trust in organic food (Roh et al., 2022). Thus, H6 and H7 are accepted.
4.3 Mediation effect test results
The mediation effects were tested using the SPSS PROCESS macro for distributed regression, and the results are presented in Table 8. Except for the relationship between low-carbon knowledge and purchase intention, which is significant at the 0.01 level, all other pathways reached significance at the 0.001 level. This indicates that attitude significantly mediates the relationship between environmental values and purchase intention, quality trust significantly mediates the relationship between environmental values and purchase intention, quality trust significantly mediates the relationship between low-carbon knowledge and purchase intention, and both environmental values and quality trust significantly mediate the relationship between low-carbon knowledge and purchase intention. Baron and Kenny observed that psychological phenomena are usually influenced by multiple factors. Therefore, a more realistic goal is to demonstrate that mediating variables significantly reduce the effect of the independent variable on the dependent variable, rather than eliminating the relationship entirely. They call this partial mediation. The results in Table 8 show that the direct effect of the independent variable on the dependent variable is significant at the 0.01 level across all four paths. This means that the mediation effects are all partial rather than full: the direct effects remain significant and there are also indirect effects through the mediators (Baron and Kenny, 1986).
Table 8. Mediation effects via bootstrap regression analysis.
Table 9 displays the analysis results of the indirect effects tested through 5,000 Bootstrap resamples. We examined the mediating roles of attitude, quality trust, and the combined effect of environmental values and quality trust in the model. The results show that the indirect effect values are 0.122, 0.216, 0.232, and 0.238, respectively, and that all mediator variables have 95% confidence intervals that do not include zero, indicating that attitude, quality trust, and the combined effect of environmental values and quality trust each play a significant mediating role in their respective paths.
Table 9. Bootstrap mediation effect test results.
According to the effect ratio calculations, the direct effect of attitude on the relationship between environmental values and purchase intention accounts for 77.76%, while the indirect effect accounts for 22.24%. The direct effect of quality trust on the relationship between environmental values and purchase intention accounts for 60.64%, while the indirect effect accounts for 39.36%. The direct effect of quality trust on the relationship between low-carbon knowledge and purchase intention accounts for 29.01%, while the indirect effect accounts for 70.99%. The direct effect of environmental values and quality trust on the relationship between low-carbon knowledge and purchase intention accounts for 27.39%, while the total indirect effect accounts for 72.61%. Specifically, the indirect effect of environmental values accounts for 14.92%, the indirect effect of quality trust accounts for 50.93%, and the combined indirect effect of environmental values and quality trust accounts for 6.76%.
5 Discussion and limitations
5.1 Theoretical implications
First, The current study examined the following: In the first stage, the relationship of subjective norms and low-carbon attitude on the purchase intention of carbon-labeling items. In the second stage, the relationship between environmental values and low-carbon attitudes and Low-carbon knowledge. In the third stage, the effect of quality trust on purchase intentions, environmental values, and Low-carbon knowledge for carbon-labeling items. This study enriches and extends the application of the TRA in green consumption research. By integrating three key variables—environmental values, low-carbon knowledge, and quality trust—into the traditional TRA framework (comprising attitude, subjective norm, and purchase intention), we construct a multi-variable interaction model that captures the complex mechanisms underlying carbon labeling consumption. The findings confirm that environmental values indirectly influence purchase intention through attitude, while low-carbon knowledge affects purchase intention via dual pathways (environmental values and quality trust). This not only verifies the TRA's explanatory power in the context of low-carbon consumption but also addresses the limitation of the traditional model's over-reliance on individual psychological factors. By incorporating environmental values and low-carbon knowledge variables, as well as trust-based signals derived from Market Signaling Theory, we expand the TRA's theoretical boundaries and provide a more comprehensive analytical framework for understanding green consumption behavior.
Second, this research fills gaps in early carbon labeling research in China and provides forward-looking empirical support for the full implementation of carbon labeling policies. Prior studies on carbon labeling in China have often focused on conceptual discussions or small-scale exploratory analyses, lacking systematic empirical evidence on consumer decision-making mechanisms—especially among knowledgeable consumers with professional backgrounds (Carrero et al., 2021; Holenweger et al., 2023; Panzone et al., 2021; Li et al., 2017; Zhao et al., 2020; Lin et al., 2022). By conducting an empirical analysis of 341 university students majoring in energy-related fields (a representative group of informed consumers), this study clarifies the key drivers of carbon-labeled product consumption in the Chinese context (e.g., quality trust exhibits the strongest direct effect on purchase intention, β = 0.511). The identification of mediation pathways (e.g., the dominant indirect effect of quality trust in the relationship between low-carbon knowledge and purchase intention) offers actionable insights for policymakers. For instance, it highlights the need to prioritize trust-building measures (e.g., standardized carbon labeling certification) and low-carbon knowledge popularization in policy design. In doing so, this study supplements the empirical foundation of China's carbon labeling research system and provides a practical reference for promoting the widespread adoption of carbon labels in the future.
Third, the findings offer valuable theoretical insights into pro-environmental consumption behavior among Chinese consumer groups. Chinese consumer groups are characterized by a focus on collective interests and social responsibility (Wang et al., 2023), and our results show that environmental values (a construct closely aligned with the behavioral norms of Chinese consumer groups) exert a strong positive effect on low-carbon attitude (β = 0.639) and quality trust (β = 0.295). This indicates that among Chinese consumer groups, shaping consumers' environmental values—rooted in a sense of responsibility toward the collective and the environment—can effectively enhance their acceptance of carbon-labeled products. Furthermore, the significant mediating role of environmental values suggests that the behavioral norms of Chinese consumer groups can be leveraged to promote low-carbon consumption by fostering shared environmental values. These findings enrich research on Chinese consumer behavior by demonstrating the unique pathways through which the behavioral characteristics of Chinese consumer groups influence green consumption, and provide practical guidance for designing targeted marketing strategies and policy interventions tailored to Chinese consumer groups.
5.2 Practical implications
This study used structural equation modeling (SEM) for the empirical analysis. The results indicate that consumers‘ intention to purchase carbon-labeling items is significantly influenced by trust in product quality (with an influence coefficient of 0.511) and attitude (with an influence coefficient of 0.354). These two factors demonstrate a strong and substantial correlation with purchase intention. This suggests that, when considering purchasing carbon-labeling items, consumers' trust in product quality and their personal attitude are two very important decision-making factors. As a market signal, carbon labeling effectively communicates a product's environmental attributes, positively stimulating consumers‘ trust in quality and attitude. The presence of carbon labels enhances consumers' confidence in a product's environmental performance, thereby promoting purchase intent. This finding provides clear, actionable guidance for both enterprises and policymakers. For businesses, the challenge is not merely to obtain a carbon label, but to leverage it as a tool for building consumer trust. This can be achieved through several concrete strategies: First, companies should enhance transparency by using QR codes on product packaging that link to detailed, third-party-verified reports on the product's life cycle assessment. Second, they can integrate the carbon label with other recognized quality certifications (e.g., ISO 9001) to create a composite signal of both environmental and quality excellence. Third, marketing campaigns should move beyond generic “green” claims and instead tell a compelling story about the specific steps taken to reduce the product's carbon footprint, thereby making the abstract concept of “low-carbon” tangible and credible for consumers. For the government and relevant industry associations, the imperative is to continuously optimize the carbon labeling system to ensure its scientific validity and authority. This involves establishing a unified, nationally recognized label to prevent market confusion, providing tax incentives or subsidies for companies that achieve certification, and creating a public, searchable database where consumers can verify the authenticity of carbon label claims. Such measures will establish the carbon label as a credible standard by which consumers can identify genuinely environmentally friendly products.
Further analysis reveals that consumers' low-carbon knowledge indirectly influences purchase intention by affecting their environmental values and trust in product quality. Notably, the influence coefficient of low-carbon knowledge on quality trust is 0.506 and that of environmental values on attitude is 0.639. This indicates that education on low-carbon knowledge and the cultivation of environmental values profoundly impact the consumption of environmentally friendly products. This study selected university students as research subjects because an individual's values are greatly influenced by their early life experiences and socialization. To effectively disseminate this knowledge, a multi-pronged approach targeting key sources is essential. Within the formal education system, curricula from primary school to university should be updated to integrate concepts of carbon footprints, life cycle thinking, and sustainable consumption not just in science classes, but also in subjects like geography, social studies, and even economics. Beyond the classroom, the media plays a pivotal role. This includes producing compelling documentaries and public service announcements, collaborating with popular science influencers on social media platforms to create engaging content, and ensuring that news coverage of climate change consistently connects the global issue to local, individual consumption choices. Therefore, disseminating low-carbon knowledge during education and cultivating positive environmental values from a young age are crucial for encouraging long-term low-carbon behavior.
Regarding awareness of carbon footprints and labels specifically, the survey results show that, while 72.4% of respondents had heard of carbon footprints, only 45.7% were aware of carbon labels. Even among students with an energy-related academic background, knowledge of carbon labeling remains limited. Given the traditional Chinese cultural tendency toward moderation, the questionnaire design specifically included‘ fuzzy memory of carbon labels' as a response option. Nevertheless, 54.3% of respondents indicated a complete lack of knowledge about carbon labels. These results suggest that the promotion and publicity of carbon labels in China urgently need to be strengthened. Bridging this awareness gap requires targeted and innovative communication strategies. Government-led campaigns could feature well-respected public figures or celebrities to endorse the carbon label, lending it social credibility and visibility. Concurrently, retailers can be incentivized to create “low-carbon” sections in their stores or online platforms, with clear signage explaining what the carbon label means. On social media, creating interactive challenges or hashtags can encourage user-generated content and peer-to-peer learning, making the promotion more organic and relatable. These concrete steps are necessary to popularize and develop the product carbon labeling certification system.
Additionally, subjective norms significantly impact consumers‘ intention to purchase carbon-labeling items. The existence of a subjective norm indicates that consumers' purchasing behavior with regard to environmentally friendly products is influenced by social norms and the opinions of others. The influence of subjective norms is particularly pronounced within the Chinese cultural context, which is characterized by collectivism and a high regard for social harmony and reputation. In this setting, green consumption can be framed not just as an individual choice, but as a responsible, modern, and respectable behavior that contributes to the national good and earns social “face.” Therefore, the government and the media should increase their efforts to promote low-carbon consumption by cultivating and reinforcing these positive social norms. This can be achieved by highlighting and celebrating role models—such as leading companies, community groups, or influential families—who have successfully adopted low-carbon lifestyles. For instance, a case study on a leading Chinese appliance manufacturer could showcase how its new line of carbon-labeled smart devices is not only technologically advanced but also aligns with the values of modern, responsible citizens. By establishing role models and promoting successful cases, more consumers can be inspired to follow suit, thereby forming a positive social trend of low-carbon consumption.
5.3 Limitations and future research
This study has some limitations, which provide directions for improvement in future research. Firstly, the measurement of environmental values in this study adopted a first-order model, which may have overlooked the interactions and influences between different values. To more comprehensively consider the impact of environmental values on consumer behavior, we suggest introducing a second-order model in future research that includes egoistic, altruistic, and ecological values to more accurately capture how these values collectively influence consumers' environmental awareness and behavior. Secondly, while the sample size in this study is sufficient for the specific population, it is relatively small compared to those of other large-scale studies. To improve the generalizability of the research, future studies should expand the scope of the sample to include more consumer groups with different characteristics and backgrounds. This would help us to gain a more comprehensive understanding of the acceptance and influence of carbon labels among different consumer groups. Thirdly, to quantify the economic impact of green consumption behavior, future studies could explore Chinese mainstream consumer groups' willingness to pay a premium for carbon-labeled items. Investigating consumers' WTP for different types of carbon-labeling items would allow us to compare and analyze these differences, providing more targeted market strategies and policy measures for businesses and policymakers.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
Ethical approval was not required for the studies involving humans because as the data does not contain any personally identifiable information, ethical approval is not required. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
LL: Writing – original draft, Writing – review & editing. KW: Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Humanities and Social Sciences Youth Foundation of the Ministry of Education of China, grant number 19YJC840023.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
Avon, Y. M. A., and Nicolas, P.. (2024). Unsustainable burgers? Deploying carbon footprint labels to enhance sustainability perceptions of animal-based food products. J. Retail. Consum. Serv. 76:103567. doi: 10.1016/j.jretconser.2023.103567
Bai, Y., and Liu, Y. (2013). An exploration of residents' low-carbon awareness and behavior in Tianjin, China. Energy Policy 61, 1261–1270. doi: 10.1016/j.enpol.2013.06.014
Baron, R. M., and Kenny, D. A. (1986). The moderator- mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51:1173. doi: 10.1037/0022-3514.51.6.1173
Carrero, I., Valor, C., Díaz, E., and Labajo, V. (2021). Designed to be noticed: a reconceptualization of carbon food labels as warning labels. Sustainability 13:15813. doi: 10.3390/su13031581
Chen, L. (2013). A study of green purchase intention comparing with collectivistic (Chinese) and individualistic (American) consumers in Shanghai. IMBR 5, 342–346. doi: 10.22610/imbr.v5i7.1061
Chen, S., and Zheng, S. (2012). The contemporary value of ecological values education for college students. Stud. Ideol. Educ. 32–35.
Chen, X., Zhen, S., Li, S., Yang, J., and Ren, Y. (2024). Consumers' willingness to pay for carbon-labeled agricultural products and its effect on greenhouse gas emissions: evidence from beef products in urban China. Environ. Impact Assess. Rev. 106:107528. doi: 10.1016/j.eiar.2024.107528
Coleman, L. J., Bahnan, N., Kelkar, M., and Curry, N. (2011). Walking the walk: how the theory of reasoned action explains adult and student intentions to go green. J. Appl. Bus. Res. 27, 107–116. doi: 10.19030/jabr.v27i3.4217
Cooper, J., and Croyle, R.-T. (1984). Attitudes and attitude change. Annu. Rev. Psychol. 35, 395–426. doi: 10.1146/annurev.ps.35.020184.002143
Ding, F., Dou, X., and Song, J. (2024). Carbon labeling system for agricultural products: components, international experience and policy implications. Environ. Protect. 52, 42–47. doi: 10.14026/j.cnki.0253-9705.2024.z1.007
Ding, Z., Jiang, X., Liu, Z., Long, R., Xu, Z., and Cao, Q. (2018). Factors affecting low-carbon consumption behavior of urban residents: a comprehensive review. Resour. Conserv. Recycl. 13, 23–15. doi: 10.1016/j.resconrec.2018.01.013
Eby, L. T., and Dobbins, G. H. (1997). Collectivistic orientation in teams: an individual and group-level analysis. J. Organiz. Behav. 18, 275–295.
Emberger-Klein, A., and Klaus, M. (2018). The effect of information provision on supermarket consumers' use of and preferences for carbon labels in Germany. J. Clean. Prod. 172, 253–263. doi: 10.1016/j.jclepro.2017.10.105
Gadenne, D., Sharma, B., Kerr, D., and Smith, T. (2011). The influence of consumers' environmental beliefs and attitudes on energy saving behaviours. Energy Policy 39, 7684–7694. doi: 10.1016/j.enpol.2011.09.002
Gu, S. (2022). The effects of subjective knowledge, altruistic value and consumer self-confidence on the green purchase attitudes and green purchase behaviour of Chinese customers. Tourism 32, 7–27. doi: 10.18778/0867-5856.32.2.01
Gu, Y., Wang, W., and Loh, H. S. (2025). Blockchain's role in low-carbon supply chain decisions with game model insights. J. Clean. Prod. 502:145356. doi: 10.1016/j.jclepro.2025.145356
He, K., Zhang, J., Zhang, L., and Wu, X. (2015). Interpersonal trust, institutional trust, and farmers' willingness to participate in environmental governance: a case study of agricultural waste resource utilization. J. Manag. World 75–88. doi: 10.19744/j.cnki.11-1235/f.2015.05.008
Holenweger, G., Sabrina, S., and Adrian, B. (2023). Carbon footprint labels involving traffic lights foster sustainable food choices. Food Qual. Prefer. 106:104813. doi: 10.1016/j.foodqual.2023.104813
Holmgren, M., Alan, K., and Patrik, S. (2017). Occupant perception of “green” buildings: distinguishing physical and psychological factors. Build. Environ. 114, 140–147. doi: 10.1016/j.buildenv.2016.12.017
Izquierdo, I., Julio, O., and Francisco-Jose, A. (2014). Exploratory factor analysis in validation studies: uses and recommendations. Psicothema 26, 395–400. doi: 10.7334/psicothema2013.349
Kautish, P., Justin, P., and Rajesh, S. (2019). The moderating influence of environmental consciousness and recycling intentions on green purchase behavior. J. Clean. Prod. 228, 1425–1436. doi: 10.1016/j.jclepro.2019.04.389
Kim, Y., and Choi, S. M. (2005). Antecedents of green purchase behavior: an examination of collectivism, environmental concern, and PCE. Adv. Consum. Res. 32, 592–599.
Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York, NY: Guiford Press.
Kramer, R.-M. (1999). Trust and distrust in organizations: emerging perspectives, enduring questions. Annu. Rev. Psychol. 50, 569–598. doi: 10.1146/annurev.psych.50.1.569
Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys: a literature review. Qual. Quant. 47, 2025–2047. doi: 10.1007/s11135-011-9640-9
Li, Q., Ruyin, L., and Hong, C. (2017). Empirical study of the willingness of consumers to purchase low-carbon products by considering carbon labels: a case study. J. Clean. Prod. 161, 1237–1250. doi: 10.1016/j.jclepro.2017.04.154
Lin, B., and Yang, M. (2022). Does knowledge really help?: the relationship between low-carbon knowledge and low-carbon behavior. J. Glob. Inform. Manag. 30, 1–22. doi: 10.4018/JGIM.308807
Lin, W., Rodolfo-M, N., and Wei, Y. (2022). Willingness to pay for a novel carbon label: a choice experiment in the US. Energy Econ. 114:106304. doi: 10.1016/j.eneco.2022.106304
Liu, T., Wang, Q., and Xu, S. (2015). International comparison of carbon labeling and its enlightenments for China. China Popul. Resour. Environ. 25, 599–601.
MacNaughton, P., Satish, U., Laurent, J. G. C., Flanigan, S., Vallarino, J., Coull, B., et al. (2017). The impact of working in a green certified building on cognitive function and health. Build. Environ. 114, 178–186. doi: 10.1016/j.buildenv.2016.11.041
Maleknia, R., Hălălis ´ an, A.-F., and Maleknia, K. (2025). Who shapes what we should do in urban green spaces? An investigation of subjective norms in pro-environmental behavior in Tehran. Forests 16:1273. doi: 10.3390/f16081273
McCarthy, M., de Boer, M., O'Reilly, S., and Cotter, L. (2003). Factors influencing intention to purchase beef in the Irish market. Meat Sci. 65, 1071–1083. doi: 10.1016/S0309-1740(02)00325-X
Mi, L., Gu, M., Yang, J., Yu, X., and Liu, Y. (2016). Empirical research on the psychological motivation factors of urban residents' low carbon oriented energy consumption behavior in Xuzhou City. Resour. Sci. 38, 609–621.
Mostafa, M.-M. (2016). Egyptian consumers' willingness to pay for carbon-labeled products: a contingent valuation analysis of socio-economic factors. J. Clean. Prod. 135, 821–828. doi: 10.1016/j.jclepro.2016.06.168
Özekici, Y. K. (2022). Extending value-belief and norm theory with social identity for preventing food waste at restaurants. Turizm Akad. Dergisi. 9, 273–291.
Paco, A., and Tania, L. (2017). Environmental knowledge and attitudes and behaviours towards energy consumption. J. Environ. Manag. 197, 384–392. doi: 10.1016/j.jenvman.2017.03.100
Pahlevi, M.-R., and Dwi, S. (2020). The integrated model of green loyalty: evidence from eco-friendly plastic products. J. Clean. Prod. 257:120844. doi: 10.1016/j.jclepro.2020.120844
Panzone, L. A., Ulph, A., Hilton, D., Gortemaker, I., and Tajudeen, I. A. (2021). Sustainable by design: choice architecture and the carbon footprint of grocery shopping. J. Public Policy Market. 40, 463–486. doi: 10.1177/07439156211008898
Rausch, T., and Cristopher-Siegfried, K. (2021). Bridge the gap: consumers' purchase intention and behavior regarding sustainable clothing. J. Clean. Prod. 278:123882. doi: 10.1016/j.jclepro.2020.123882
Roh, T., Junhee, S., and Yaeri, K. (2022). Unveiling ways to reach organic purchase: green perceived value, perceived knowledge, attitude, subjective norm, and trust. J. Retail. Consum. Serv. 67:102988. doi: 10.1016/j.jretconser.2022.102988
Ruscio, J., and Brendan, R. (2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychol. Assess. 24, 282–292. doi: 10.1037/a0025697
Saeed, M., Ahmed, A., Bogan, E., Abul, O., and Qammar, A. (2025). ‘Buying green': the role of green customer-based brand equity in shaping customers' green buying behavior. Environ. Dev. Sust. 27, 5981–5997. doi: 10.1007/s10668-023-04110-2
Salmela, S., and Vilja, V. (2006). Consumers in the green electricity market in Finland. Energy Policy 34, 3669–3683. doi: 10.1016/j.enpol.2005.08.008
Shuai, C., and Zhang, Y. (2013). Variance analysis of consumers' willingness to pay for low-carbon products in china based on scenario experiment with carbon labeling. China Soft Sci. 61–70.
Singh, A., and Priyanka, V. (2017). Factors influencing Indian consumers' actual buying behaviour towards organic food products. J. Clean. Prod. 167, 473–483. doi: 10.1016/j.jclepro.2017.08.106
Song, Z., Hu, M., Leng, M., and Zhu, S. (2024). Exploring the effects of low-carbon labels on purchase intentions for green agricultural products. Sustainability 16:7313. doi: 10.3390/su16177313
Sörqvist, P., Haga, A., Langeborg, L., Holmgren, M., Wallinder, M., Nöstl, A., et al. (2015). The green halo: mechanisms and limits of the eco-label effect. Food Qual. Prefer. 43, 1–9. doi: 10.1016/j.foodqual.2015.02.001
Steg, L. (2008). Promoting household energy conservation. Energy Policy 36, 4449–4453. doi: 10.1016/j.enpol.2008.09.027
Sun, M., Gao, X., Jing, X., and Cheng, F. (2023). The influence of internal and external factors on the purchase intention of carbon-labeled products. J. Clean. Prod. 419:138272. doi: 10.1016/j.jclepro.2023.138272
Sureeyatanapas, P., Kanittha, Y., and Viganda, V. (2021). Drivers, barriers and benefits of product carbon footprinting: a state-of-the-art survey of thai manufacturers. Sustainability 13:654312. doi: 10.3390/su13126543
Tan, C.-S., Hooi-Yin, O., and Yen-Nee, G. (2017). A moral extension of the theory of planned behavior to predict consumers' purchase intention for energy-efficient household appliances in Malaysia. Energy Policy 107, 459–471. doi: 10.1016/j.enpol.2017.05.027
Vandenbergh, M., Thomas, D., and Paul-C, S. (2011). Commentary: time to try carbon labelling. Nat. Clim. Change 1, 4–6. doi: 10.1038/nclimate1071
Wang, C. P., Zhang, Q., Wong, P. P. W., and Wang, L. (2023). Consumers' green purchase intention to visit green hotels: a value-belief-norm theory perspective. Front. Psychol. 14:1139116. doi: 10.3389/fpsyg.2023.1139116
Wang, L., Wong, P. P. W., and Elangkovan, N. A. (2020). The influence of religiosity on consumer's green purchase intention towards green hotel selection in China. J. China Tour. Res. 16, 319–345. doi: 10.1080/19388160.2019.1637318
Wang, Z., Dongxue, G., and Xiaomeng, W. (2016). Determinants of residents' e-waste recycling behaviour intentions: evidence from China. J. Clean. Prod. 137, 850–860. doi: 10.1016/j.jclepro.2016.07.155
Xiong, M., Sun, S., Feng, J., Qian, H., and Ke, S. (2014). Investigation of college students' low-carbon awareness and behavior. China Popula. Resour. Environ. 24, 248–251.
Xu, M., and Boqiang, L. (2021). Leveraging carbon label to achieve low-carbon economy: Evidence from a survey in Chinese first-tier cities. J. Environ. Manag. 286:112201. doi: 10.1016/j.jenvman.2021.112201
Yang, B. (2015). Working of green food market and consumers' choice in the low trust condition on eco-label. Econ. Survey 32, 73–78.
Yang, M., and Lin, B. (2024). Differences in willingness to pay for various types of carbon-labeled products: a dynamic perspective. J. Environ. Manag. 370:122936. doi: 10.1016/j.jenvman.2024.122936
Yuan, R., and Tang, Z. M. (2025). Does carbon labelling encourage the consumption of low-emission meat products? Evidence from China. Agric. Resour. Econ. 69, 80–99. doi: 10.1111/1467-8489.12602
Zauskova, A., Miklencicová, R., Madlenák, A., Bezáková, Z., and Mendelová, D. (2013). Environmental protection and sustainable development in the Slovak Republic. Eur. J. Sci. Theol. 9, 153–159.
Zhang, X., Wang, Y., Wei, H., Wang, Y., Zhao, X., Xue, J., et al. (2017). The impacts and countermeasures of Carbon Label on import and export trade of China's agricultural products. China Popul. Resour. Environ. 27, 10–13.
Zhao, R., Dingye, W., and Junke, Z. (2021). Policy implications on carbon labeling scheme toward carbon neutrality in China. Front. Environ. Sci. 9:739943. doi: 10.3389/fenvs.2021.739943
Zhao, R., Geng, Y., Liu, Y., Tao, X., and Xue, B. (2018). Consumers' perception, purchase intention, and willingness to pay for carbon-labeled products: a case study of Chengdu in China. J. Clean. Prod. 171, 1664–1671. doi: 10.1016/j.jclepro.2017.10.143
Zhao, R., Yang, M., Liu, J., Yang, L., Bao, Z., and Ren, X. (2020). University students' purchase intention and willingness to pay for carbon-labeled food products: a purchase decision-making experiment. Int. J. Environ. Res. Public Health 17:702619. doi: 10.3390/ijerph17197026
Zhou, S., Wang, H., Li, S., Chen, Y., and Wu, J. (2019). Carbon labels and “horizontal location effect”: can carbon labels increase the choice of green product? Glob. Ecol. Conserv. 18:e00609. doi: 10.1016/j.gecco.2019.e00609
Zou, J., Kamarudin, K. M., Liu, J., and Zhang, J. (2024). Towards sustainable mobility: determinants of intention to purchase used electric vehicles in China. Sustainability 16:858819. doi: 10.3390/su16198588
Appendix
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Keywords: carbon labeling items, environmental values, low-carbon knowledge, quality trust, Theory of Reasoned Action
Citation: Liu L and Wang K (2026) The role of carbon labels for consumer decisions: evidence from a class of Chinese students. Front. Clim. 7:1708974. doi: 10.3389/fclim.2025.1708974
Received: 30 September 2025; Revised: 20 December 2025;
Accepted: 29 December 2025; Published: 16 January 2026.
Edited by:
Kostoula Margariti, University of Macedonia, GreeceReviewed by:
Arry Widodo, Telkom University, IndonesiaMeng Sun, Xi'an University of Architecture and Technology, China
Copyright © 2026 Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kaiqing Wang, d2txMDEwNzA2QDE2My5jb20=