From Green Awareness to Green Behavior: The Impact of Information Disclosure Scenarios on Greener Shopping Channel Choices

Abstract

Addressing climate change necessitates reducing carbon emissions, with green behavior adoption being crucial. This study examines how green consumption awareness (GCA) and carbon emission disclosures influence consumer shopping channel choices, offering a practical approach to converting awareness into actionable behavior. Using stated preference (SP) data, we investigate the impact of green awareness and information disclosure on consumers’ choices between online and offline shopping channels. The key findings include the following: (1) GCA affects shopping channel choices in certain scenarios, though not always significantly. (2) Detailed carbon information disclosure steers consumers towards lower-emission channels, especially when specific carbon data are provided. (3) The type of goods significantly influences shopping channel decisions, serving as a variable across scenarios. (4) Effective scenarios, such as a 3 km shopping trip for categories like tissue and trash bags, where the difference in channel choice under varying levels of information disclosure is statistically significant, have been identified. These insights inform recommendations for information disclosure strategies that not only enhance GCA but also drive behavioral change, thereby fostering environmentally friendly consumption behaviors that contribute to a reduction in consumers’ carbon footprint.

1. Introduction

In the quest to mitigate the pervasive challenge of climate change, the reduction in carbon emissions has emerged as a paramount objective within the global environmental agenda. This imperative is driven by a consensus among scientists and policymakers alike that significant reductions in greenhouse gas emissions are essential to prevent the catastrophic impacts of climate change, as outlined in international agreements such as the Paris Agreement [1].

Within the ambit of this global endeavor, the modulation of individual lifestyles emerges as a vital component of the broader strategy to mitigate environmental degradation. Such modulation encompasses a diverse array of interventions aimed at encouraging more environmentally sustainable behaviors among individuals. Scholars have discovered a series of intervention measures through research. These interventions include, but are not limited to, promoting greener travel modes considering “greener” awareness [2,3] and various psychological and behavioral determinants, such as ascription of responsibility, awareness of consequences, and perceived usefulness [4]. Lanzini and Khan [5] foster the adoption of renewable energy sources using psychological empowerment [6], encouraging the reduction in waste through recycling and composting initiatives through social responsibility [7] and social psychology [8]. Reese [9] found that provincial regulations will further reduce the use of towels and promote environmental behavior in hotels by comparing the psychological effects of different social norms on the use of hotel towels. By integrating these theories and practices into the fabric of daily life, the collective impact of psychological perception and cognitive processing can be translated into individual behaviors that significantly contribute to the overarching goal of carbon footprint reduction. However, translating green awareness into concrete consumer behavior remains a complex and unexplored field. This study aims to address this gap by investigating the impact of green consumption awareness and carbon emission disclosure on consumers’ shopping channel choices.

Shopping channel choice constitutes a fundamental component of consumer lifestyles, mirroring individual preferences and values within the context of everyday activities. As consumer behavior continues to evolve, shopping has significantly transformed [10,11], especially with the expansion of online commerce. According to Yaguara [12], the projection of U.S. online shopping user size from 2020 to 2026, as illustrated in Figure 1a, indicates that there were 274.70 million online buyers in the United States in 2023, data from 2024–2026 are forecast data, which is represented by ‘’* ‘’in the upper right corner of Figure 1a. This figure represents more than 81% of the total U.S. population engaging in online shopping. Furthermore, Figure 1b outlines the top 5 global e-commerce retailers based on their sales in 2022, with Alibaba at the forefront as the world’s leading e-commerce retailer, amassing USD 780.70 billion in e-commerce sales for the year. These insights highlight the growth of the online shopping sector, demonstrating an increasing trend of internet users participating in e-commerce activities.

This digital expansion not only has revolutionized access to goods and services but has also redefined the supply chain logistics, engendering distinct variations in carbon emission profiles associated with different channels of shopping [13]. This difference has also become a key area of concern for scholars [14].The divergent environmental impacts of online versus traditional in-store shopping choices highlight a critical opportunity for guiding consumers’ choices towards sustainability. Central to this opportunity is the concept of “GCA”, an emerging psychological perception among consumers regarding the environmental implications of their purchasing decisions. This psychological perception is pivotal in leveraging differences in carbon emissions between online and traditional shopping, presenting a viable pathway to influence consumer behavior. This is a factor that has not been extensively studied in the literature.

However, while psychological perception is a crucial driver of behavior, the choice of shopping channel can be influenced by a broader range of factors beyond a single type of psychological perception, such as price, convenience, and product type [15,16]. The actual impact of GCA on shopping channel choices remains empirically under-examined, highlighting a significant gap between the concept of utilizing GCA and its practical application. Thus, it is essential to foster a deeper understanding of GCA among consumers. Through this study, exploring the subtle relationship between GCA, carbon information disclosure, and shopping channel choices can help enrich and improve the existing body of knowledge. Additionally, being informed about which shopping channels are more eco-friendly is a prerequisite for transferring green awareness into greener shopping behaviors. Therefore, a more detailed investigation into the impact of GCA, particularly at varying levels of carbon emission information disclosure, on shopping channel choices is necessary. By doing so, GCA can be effectively implemented not only to influence shopping channel choices but also to better align these choices with the objectives of environmental sustainability. In essence, while the leveraging of GCA holds promise as a strategy for promoting sustainable consumer practices, its efficacy is contingent upon addressing a set of pertinent research questions. These include the following:

(1)

Does GCA exist among consumers?

(2)

Does GCA significantly affect the choice of shopping channel behavior, or do alternative factors dominate and mitigate the practical influence of this environmentally conscious mindset on their choices of shopping channels?

(3)

What is the potential role of different levels of disclosure of carbon emission information in informing and guiding consumers’ choices of greener shopping channels?

(4)

Under which conditions does this green awareness catalyze actionable changes in green behavior that align with environmental sustainability objectives?

The exploration of these questions is vital for delineating the contours of effective strategies aimed at enhancing the role of GCA in driving environmentally sustainable shopping behaviors. By addressing these critical questions, this paper endeavors to deepen the understanding of the mechanisms through which the psychological perception of consumers, namely, the awareness of green consumption, can be translated into effective environmentally friendly behavior, thereby contributing to the broader goal of carbon emission reduction. Through a comprehensive examination of these issues, our interdisciplinary approaches and methodologies have made substantial contributions to the emerging field of sustainable consumer behavior, enriching relevant literature and providing a deeper understanding of the mechanisms between psychological perception and environmentally friendly behavior. Leveraging interdisciplinary approaches and methodologies, the present study seeks to substantively contribute to the burgeoning field of environmental psychology and sustainable consumer behavior. By examining the psychological and contextual determinants of green consumption choices and assessing the effectiveness of information disclosure in promoting low-carbon shopping behaviors, this research aims to provide actionable insights that could inform policymaking and guide consumers towards a low-carbon lifestyle, thereby fostering a sustainable future.

The remainder of this paper is organized as follows: Section 2 provides a review of the relevant literature. The methodology employed is outlined in Section 3. Section 4 discusses the findings, while Section 5 engages in a discussion of these results. Concluding remarks and recommendations for future research are presented in Section 6.

2. Literature Review

The existing literature pertinent to this paper can be categorized into three main areas, namely, studies on the effect of different shopping channels on carbon emissions, studies on the factors influencing consumers’ choice of shopping channels, and studies on GCA.

2.1. The Effect of Different Shopping Channels on Carbon Emission

Many scholars have studied the effects of different shopping channels of consumers on carbon emissions mainly from an empirical perspective. For example, some studies have analyzed the variation in carbon emissions associated with online versus offline shopping through questionnaires and data statistics [17,18] and have found that online shopping can considerably lower carbon emissions. Rai et al. [19] compiled a collection of data records of over 200 purchases to compare the carbon footprints of online and offline shopping and found that, in some specific scenarios, online shopping tends to have lower carbon emissions than offline. Zhao et al. [20] modeled the difference in carbon emissions between online and offline shopping channels, and similarly found that the environmental costs of online shopping are cheaper, adding that, as the share of online shopping channels increases, the carbon emissions of the whole retail sector decrease.

Some studies indicate that both the location and type of commercial centers have a significant impact on carbon emissions [21]. Additionally, Zhang and You [22] studied omnichannel retailing, which was conducted under three different decision scenarios, namely, decentralized decision making and centralized decision making in the physical channel and the online channel. These results showed that the retailer’s earnings (whether physical or online) and the total profit across both channels would decline as consumer demand preferences and the level of emission reduction increased. This shows that different shopping channels and various aspects of the consumption process may have different levels of impact on carbon emissions.

The existing literature reveals differences in carbon emissions across various shopping channels. By understanding these differences in consumer choices and carbon emissions, it is possible to leverage consumer green awareness and guide consumers towards more eco-friendly shopping channel choices, thereby reducing their carbon emissions.

2.2. The Factors Influencing Consumers’ Choice of Shopping Channel

Existing studies have analyzed the factors that influence consumers’ choice of shopping channels. De et al. [23] emphasized that price is the key factor in this decision. To capture market share, online shopping retailers continue to cater to consumers’ preference for low prices at this stage by further lowering product prices. In response to this competition, brick-and-mortar stores also attempt to reduce prices, but due to higher costs compared with online stores, their ability to lower prices is limited. Consequently, the price war is more advantageous for online shopping [24]. If the trend of price competition continues, the market share of online shopping will further expand. Many consumers are more concerned about the delivery time of online purchases, as the wait can cause anxiety, which in turn may affect customer satisfaction and loyalty [25,26]. Commodities cannot be guaranteed to be free from damage during transportation, and furthermore, not all commodities have the guarantee of providing freight insurance. However, brick-and-mortar stores can always obtain commodities at once so that consumers can enjoy the commodities they buy directly.

Commodity variety is another important factor. Lee et al. [27] revealed the impact of product type on consumers’ choice of shopping channels through a comparative study of different product categories. Guo et al. [28] investigated the key factors influencing consumers’ intention to purchase agricultural products online using factor analysis and multiple linear regression models. Their research identified that factors such as health awareness, store reputation, price sensitivity, convenience, familiarity with the online shopping process, and expenditure on agricultural products significantly affect consumers’ purchasing decisions, with varying degrees of impact. Zhai et al. [29] conducted a comparative study in China, analyzing shopping behaviors associated with search goods (such as books) and experience goods (such as clothing) and exploring the impact of pre-purchase behaviors (such as product awareness, information search, and product trials) on online shopping. The study results indicate that the stickiness between pre-purchase channels and transaction channels is stronger for experience goods than for search goods. For experience goods, physical stores as pre-purchase channels are more effective in promoting cross-channel shopping, whereas the opposite trend is observed for search goods. Bronnenberg [30] suggests that online shopping is more favorable and advantageous for search goods, as online information searches, sharing, and comparisons can significantly reduce the quality uncertainty associated with search goods. However, this advantage does not apply to experience goods [31]. Online grocery shopping is perceived to carry a higher risk in terms of product quality compared with in-store shopping, and this perception of risk may deter consumers from purchasing groceries online [32,33]. Additionally, the quantity of groceries being purchased may also drive the choice of shopping channel.

While shopping channels can have an impact on carbon emissions, increasing awareness of green and low-carbon consumption is also shaping consumers’ shopping channel choices. According to a report from China’s Ministry of Ecology and Environment, the number of people adopting green consumption practices has been steadily increasing, with 93.3% of respondents acknowledging the importance of green consumption. Sekhokoane et al. [34] employed hypothesis testing to demonstrate that GCA significantly affects consumers’ choice of shopping channels, specifically noting differences between products that are primarily searched for online and those that are typically experienced in person.

In these studies, the researchers mainly discussed the influencing factors of different types of commodity purchase modes. Price and lead time of online orders are also studied [24]. However, there are fewer studies on the influence of GCA on consumers’ shopping channel choices, and in particular, a gap exists in exploring the mechanism of its role in specific contexts.

2.3. Green Awareness

Green awareness has recently emerged as a prominent topic of discussion in different fields. In terms of state behavior, Shao [35] found that the state has increased its attention to the environment, green consumption, and other aspects as well as publicity efforts to promote the adoption of green low-carbon behavior. Han et al. [36] found that higher consumer green sensitivity and moral levels may not always incentivize firms to exceed regulatory green standards, especially when environmental subsidies are involved.

At the individual level, this awareness drives the transformation and upgrading of Chinese enterprises to better meet consumers’ needs [37]. Wong [38] suggests using talks, videos, and exhibitions to raise public awareness of waste management and sustainable food consumption. Liao et al. [39] explored how social green preferences influence individual green behavior. High social green preferences, especially when transmitted by high-status individuals, significantly boost green motivation and actions, revealing important social dynamics in promoting sustainable practices. In terms of social behavior, Abhijith et al. [40] highlight the success of citizen science in addressing air pollution. Their study, involving public engagement and real-time air quality monitoring, successfully raised awareness and provided actionable insights into indoor and outdoor pollution levels. Conte et al. [41] explored interdisciplinary approaches to nature-based solutions and environmental awareness. The Aula Verde Aniene project in Rome, combining ecology, urban forestry, and various art forms, engaged the community through art and citizen science, enhancing the perception of ecosystem services and fostering a sense of belonging to nature. Liang et al. [42] examined how the integration of intelligent technologies affects the environmental performance of power substations in China. They found that higher intelligence levels significantly enhance the environmental performance of substation projects by reducing material waste and pollutant emissions. This finding implies that the adoption of intelligent systems in substation projects can directly contribute to greener construction practices, as the efficiencies gained through intelligentization not only reduce the consumption of materials but also minimize the environmental impact. Therefore, integrating intelligentization into construction projects can serve as a practical strategy for promoting sustainable development within the construction industry. Using SmartPLS partial least squares structural path modeling on data from questionnaires, Cheng et al. [43] examined the effect of green process innovation and eco-friendly production on the sustainability of cement and plastic manufacturing in Pakistan and India. The study found that green productivity and green process innovation significantly enhanced sustainability. Khalil et al. [44] investigated the influence of awareness regarding social and environmental sustainability practices on impulse buying. They found that social and environmental sustainability awareness positively influences green trust, which in turn enhances impulse buying. The study strongly advises that policymakers and marketers prioritize efforts to enhance awareness of environmental and social sustainability, along with fostering green altruism. Shang et al. [45] used the Theory of Planned Behavior as a conceptual framework to explore the impact of sustainability awareness on green purchase intention. The study results indicate that green purchase intention is influenced by green attitude, subjective norms, perceived behavioral control, and the positive and negative modulation of sustainability awareness. This research makes a significant contribution to promoting environmental conservation and sustainable consumption.

2.4. Research Gaps and Study Contributions

The gaps in existing research and the contributions of this paper are as follows:

(1)

Existing literature reveals differences in carbon emissions across various shopping channels. However, despite the progress in understanding these differences in consumer choices and carbon emissions, there remains a gap in leveraging consumer green awareness and guiding them towards more eco-friendly shopping channel choices, thereby reducing their carbon emissions. To fill this gap, we aim to transition from green awareness to green behavior by studying the impact of information disclosure scenarios on greener shopping channel choices.

(2)

In these studies, the researchers mainly discussed the influencing factors of different types of commodity purchase modes. The factors of price and receipt interval of different purchase modes are also studied [24]. However, there are fewer studies on the impact of green consumption awareness on consumers’ shopping channel choices, and in particular, a gap exists in exploring the mechanism of its role in specific contexts. In this paper, we collect data on the importance of various factors influencing shopping channel choices, including the key influencing factors and green consumption awareness, using a slightly modified 5-point Likert scale. This approach allowed us to first validate green consumption awareness.

(3)

Existing research highlights the impact of green awareness across various behavioral domains. However, there is still a gap in understanding the consistency and effectiveness of green awareness. Additionally, there is a notable scarcity of studies exploring how varying levels of carbon information disclosure influence behavioral choices and changes, especially within the realm of shopping channel choice. Building upon this foundational research, this paper examines GCA among consumers and assesses whether different levels of carbon information disclosure have distinct behavioral impacts, using methods such as the SP survey, descriptive analysis, and consistency test. We adopt a practical approach, aiming to extend green consumption awareness beyond mere psychological perception or cognitive processing to actively induce behavioral change. This involves identifying effective disclosure scenarios where green consumption awareness significantly influences shopping channel decisions, thereby advancing the global environmental agenda. This study enriches the literature on green consumption awareness and carbon disclosure behavior, providing enhanced guidance to residents and supporting the achievement of carbon emission reduction.

3. Materials and Methods

The primary objective of this paper is to address four critical questions discussed in Section 1. The first question, “Does GCA exist among consumers?”, serves as the precondition for transferring green awareness into green behavior. To explore this, we will design related questions in the questionnaire, which will be discussed further in Section 3.2 and Section 4.1. The second and third critical questions, “Can awareness of green consumption influence the choice of shopping channels?” and “Is the level of disclosure of carbon information a significant factor?”, will be investigated through hypotheses as outlined in Section 3.1. Corresponding testing will be conducted in Section 4.2 and Section 4.3, utilizing the stated preference (SP) survey data discussed in Section 3.2. Furthermore, regarding the fourth question, “Under which conditions does this green awareness catalyze actionable changes in green behavior that align with environmental sustainability objectives?”, we will conduct hypothesis tests to identify potential variables across different scenarios where the effect of green awareness on green shopping channel choice behavior could vary, as discussed in the third hypothesis in Section 3.1. Following this, we will perform scenario analysis in Section 5 to identify effective scenarios for disclosing different levels of information on the carbon emissions of shopping channels and propose related policy recommendations.

3.1. Consistency Tests and Hypothesis Development

Consistency tests, also known as homogeneity tests, are pivotal in verifying the uniformity of distributional properties or variability within datasets. These tests are crucial in determining whether different measurements or conditions yield consistent results and whether consistency is maintained across various studies to justify their integration or comparison. Grounded in mathematical statistics and probability theory, consistency tests often involve hypothesis testing—which sets a null hypothesis (H0) and computes a test statistic from the data, subsequently comparing it with a threshold value to either reject or fail to reject the hypothesis [46,47]. This paper predominantly applies SPSS software 25.0 to the SP data, focusing on mean comparisons using one-way ANOVA and independent samples t-tests to evaluate our hypotheses.

Here, a one-way ANOVA test is used for these two hypotheses. Here, “H1” represents the alternative hypothesis.

(1)

The influence of awareness of GCA and carbon emission information disclosure on choice of shopping channels

H0: 

There is no difference in shopping channel choices between consumers who have been provided with carbon information for different shopping channels and those who have not received such information.

H1: 

There is a significant difference in shopping channel choices between consumers who have been provided with carbon information for different shopping channels and those who have not received such information.

(2)

The influence of varying levels of carbon emission information disclosure on the choice of shopping channels

H0: 

There is no difference in shopping channel choices among consumers who have received varying levels of carbon information disclosure.

H1: 

There is a significant difference in shopping channel choices among consumers who have received varying levels of carbon information disclosure.

Another objective of this paper is to identify effective disclosure scenarios where awareness of green consumption and the disclosure of carbon emission information significantly influence consumer behavior. Therefore, it is crucial to determine which variables need to vary across different scenarios. Four additional determinants of shopping channel choice besides carbon emissions will be discussed in Section 4.1, namely, shopping trip length (travel time and travel cost), price, quality, and lead time for online orders. In this analysis, shopping trip length is treated as a variable varying across different scenarios. However, the remaining factors—price, quality, and lead time—are inherent characteristics of both online and offline shopping channels and will not be altered in the development of policies designed to direct consumer choices towards a channel with reduced carbon emissions. Thus, these factors are not considered variables in our scenario analysis.

Nevertheless, beyond the inherent differences between online and offline shopping, the type of goods represents another potential variable that might influence the effectiveness of GCA campaigns. It is essential to determine whether this factor has an impact on shopping channel choice. Should it not influence decision making, it may be excluded from consideration. Conversely, if it proves significant, it is imperative to incorporate the type of goods as a crucial variable in crafting scenarios where awareness of green consumption markedly affects shopping channel decisions. For this analysis, we employed an independent samples t-test to assess the consistency between two types of goods. The hypotheses formulated for this purpose are as follows:

(3)

The influence of different types of goods purchased on the choice of shopping channels

H0: 

There is no difference in shopping channel choices when purchasing different types of goods.

H1: 

There is a significant difference in shopping channel choices when purchasing different types of goods.

The overview of the proposed hypothesis is shown in Figure 2.

3.2. Questionnaire Design

The questionnaire is divided into three primary sections: the first part collects basic personal information from participants, such as gender and age; the second part assesses the importance of various factors that consumers perceive as influencing their choice of shopping channels in daily life using the Likert scale, which is discussed further in Section 4.1; and the third part involves a SP survey designed to gather data on consumer preferences for shopping channels across different categories of goods (commodities) within four shopping trip length ranges (0.5 km, 3 km, 5 km, and 10 km). Additionally, the SP survey collects choices of shopping channels under varying conditions, including three carbon emission disclosure scenarios (detailed in

Table 1. Carbon emission disclosure scenarios.

Carbon Emission Disclosure Scenarios	Description
Scenario 1	No Disclosure: No information on the difference in carbon emissions between online and offline shopping is disclosed (corresponding to Q6–9 in Appendix A).
Scenario 2	Partial Disclosure: It is disclosed which of the two shopping channels, online or offline, incurs higher carbon emissions (corresponding to Q10–13 in Appendix A).
Scenario 3	Full Disclosure: A comparison of carbon emissions between online and offline shopping channels, including precise values of the differences between the two., is provided (corresponding to Q14–17 in Appendix A).

) and four types of shopping goods, resulting in a total of 48 scenarios. To ensure consistency across scenarios, the types of commodities presented in each are identical. The complete survey questionnaire is provided in Appendix A.

3.3. Data Collection

The data utilized in this study were gathered through online SP surveys administered by “wjx” (https://www.wjx.cn/, accessed on 31 August 2024), a platform specializing in online surveys, assessments, and voting mechanisms. This method of data collection is very common, such as that described by Yang et al. [48]. After finalizing the questionnaire design, it was distributed online and targeted at various age groups. To encourage greater participation, we shared the questionnaire across multiple channels, including “WeChat Moments” and several friend groups, and by actively reaching out to classmates, who further requested their friends and family members to participate. The survey collected responses from 248 participants. After excluding responses that either were too quick or exhibited highly consistent choices, 230 valid questionnaires remained. The age distribution showed that the largest group of respondents were aged 45–54, comprising 29.6% of the total, followed by those aged 18–24, who made up 24.8% of the respondents. The educational qualifications of the respondents were primarily composed of bachelor’s degrees and levels below a bachelor’s degree (excluding bachelor’s degrees), accounting for 40.9% and 54.8%, respectively. Moreover, the questionnaire encompassed 48 scenarios, as detailed in Appendix A. Given the number of valid questionnaires, the scale of valid data amounts to 11,040.

4. Analysis of Results

4.1. The Existence of GCA and Its Impact on Shopping Channel Choice

In this section, there are two primary objectives. The first one is to confirm the existence of GCA while choosing a shopping channel. We frame the inquiry as “How important are lower carbon emissions as a factor influencing your choice of shopping channel?” rather than directly asking, “Do you have GCA”. This method is chosen to avoid leading responses, as direct questions about GCA might predispose respondents to declare a higher level of awareness. This careful phrasing is intended to minimize any inadvertent influence on the outcomes, thus ensuring more genuine and unbiased responses.

Additionally, the second objective is to determine whether this awareness is as significant as other factors such as price, quality, lead time of online orders, and shopping trip length (i.e., travel time and travel costs), as discussed in Section 2.2. The importance of this investigation lies in determining whether carbon emissions are regarded as a critical factor. If carbon emissions are considered one of the most important indicators, it suggests that GCA could universally translate into behavior that influences shopping channel choices. Conversely, if it is not deemed crucial, GCA may only be effective in specific scenarios, remaining merely a psychological perception or cognitive process without translating into actionable green behavior.

Specifically, in this section, we collected data on the importance of various factors influencing shopping channel choices using a slightly modified 5-point Likert scale. The points on the scale are labeled as follows: 1 for “Very Important: Crucial”; 2 for “Important: Significant”; 3 for “Moderately Important: Considerable”; 4 for “Slightly Important: Marginal”; and 5 for “Not Important: Negligible”, as shown in Question 5 in Appendix A.

As for the first objective, we conducted an initial analysis to identify the reference factors considered by consumers when choosing shopping channels. The questionnaire design incorporated five influencing factors. The percentage of participants selecting each degree of importance for each factor is depicted in Figure 3.

We then analyzed the data from two perspectives. On the one hand, we evaluated the importance of the factor “lower carbon emissions of the shopping channel” individually, where scores closer to 1 indicate a stronger awareness or cognition related to GCA, as shown in Figure 3d. Of the participants, 43% selected “1”, “2”, “3”, or “4”, indicating that they possessed a certain level of awareness of green consumption at the psychological level. Therefore, the presence of GCA is evident to some extent, but further research is needed to determine whether this psychological perception or cognitive processing can lead to behavioral change. Understanding the practical implications of GCA is crucial for assessing its real-world impact.

We, on the other hand, conducted a horizontal comparison across different factors to evaluate their relative importance. This approach helps us determine whether certain factors tend to dominate others in influencing shopping channel choice. According to Figure 3, the percentage of respondents selecting “1” for the factor “carbon emissions” was lower compared with other factors.

To facilitate a more direct comparison, we conducted a descriptive analysis to elucidate the overall characteristics of the dataset using measures such as the mean value and standard deviation, as presented in

Table 2. Descriptive analysis of reference factors.

Factors	Sample Size	Mean Value	Standard Deviation	Variance
Price	230	2.00	0.874	0.764
Quality	230	1.44	0.695	0.484
Lead time of online order	230	3.40	0.829	0.686
Impact of each shopping channel on carbon emission	230	4.40	0.844	0.712
Shopping trip length (travel time and travel cost)	230	3.76	1.130	1.277
Number of valid responses	230

.

As shown in Table 2, it emerges that the “quality” factor is deemed the most significant, with a mean value of 1.44, whereas “the impact of carbon emissions”—the factor reflecting GCA—is considered the least important, also with a mean value of 4.4. Notably, the “quality” factor exhibits the lowest standard deviation at 0.695, reflecting a greater consensus among respondents regarding its significance. In contrast, the factor of “shopping trip length (travel time and travel cost)” displays the highest standard deviation of 1.277, indicating substantial variation in perceptions of its importance among the participants.

This indicates that the consumers surveyed typically prioritize other factors, especially quality over the impact of carbon emissions, when choosing a shopping channel. There is a tendency among consumers to purchase “what I need”, overlooking carbon emission standards in the current economic context, making GCA a marginal factor in shopping channel choice. In this scenario, GCA is evidently an existing psychological perception. Although the current focus on the impact of carbon emissions is minimal, there is potential for further engagement and guidance.

However, the lingering question remains: will it perpetually remain as a non-functional psychological phenomenon, overshadowed by other factors? Can this psychological perception be translated into green behavior? Moreover, it is essential to determine the circumstances under which GCA can impact shopping channel choices.

4.2. The Influences of GCA and Carbon Emission Information on Shopping Channel Choices

In the scenarios with partial disclosure, corresponding to Q10–Q13 in Appendix A, objective information alone does not influence shopping choice behavior; rather, it is the psychological impact of GCA that potentially affects shopping channel choices. Additionally, since there are no established guidelines for green shopping channel choices, it can be assumed that, without disclosing the carbon emissions of different shopping channels, GCA may not be effectively expressed in the decision-making process for shopping channel choices. This suggests that while green awareness might exist as a psychological perception, it could remain unnoticed and unexpressed in actual shopping behaviors.

Consequently, by examining the differences in shopping choices between the partial disclosure and no disclosure scenarios, we can ascertain whether GCA can influence shopping channel choice behavior. Similarly, by examining the differences in shopping choices between the full disclosure and no disclosure scenarios, we can further test the joint influence of GCA and carbon emission information disclosure on the choice of green shopping channels. This approach allows us to delineate the impact of GCA on the choice of shopping channels, specifically determining whether GCA can be translated into green shopping channel choice behavior.

4.2.1. Descriptive Analysis

Initially, a descriptive analysis is conducted on the choices of shopping channels under two specific demands to preliminarily assess whether GCA and carbon emission information could jointly influence consumers to select shopping channels with lower carbon emissions, as discussed in Section 3.3. For this purpose, “purchasing a fabric sofa at 5 km” and “purchasing an air conditioner at 10 km” are selected as examples. These examples are utilized to compare survey data on the number of consumer choices for shopping channels under three carbon emission disclosure scenarios, as illustrated in Figure 4.

As can be seen, Figure 4a,b suggest an observable shift in consumers’ green behaviors towards channels with lower carbon emissions upon receiving disclosure information. In the case of purchasing a fabric sofa at 5 km (Figure 4a), the number of consumers opting for the online shopping channel, which is associated with lower carbon emissions as per Appendix A, increases from no disclosure to full disclosure scenarios. Initially, in the absence of any carbon emission information, the number of consumers opting for online shopping stands at 95, which is lower compared with those preferring offline channels (135). Nevertheless, with the introduction of partial disclosure, a significant shift towards the online channel is evident, and this trend intensifies further with the move to full disclosure.

Likewise, in the case of purchasing an air conditioner at 10 km (Figure 4b), there is a clear trend towards a greater preference for the greener online channel as the level of information disclosure increases. In the scenario without any disclosure, the distribution of consumers between the offline (116) and online (114) shopping channels is nearly balanced. However, with the introduction of partial disclosure, the number of consumers opting for green online shopping rises to 126, and this figure further increases to 130 under full disclosure. These shifts highlight the efficacy of information disclosure in transferring GCA from psychological perception to green consumer behavior towards more environmentally friendly channel choices. We will proceed to conduct further tests to determine the statistical significance of the hypothesis.

4.2.2. Consistency Test

In Section 4.2.1, we analyze the initial finding that awareness of green consumption leads consumers to choose shopping channels with lower carbon emissions. Building upon this observation, we further examine the statistical significance of this phenomenon. Utilizing SPSS software to analyze the data obtained from the SP survey, we first conduct a chi-square test across several scenarios discussed in Section 4.2.1. The significance values from this test are all greater than 0.05, indicating that the hypothesis of the chi-square test could not be rejected and that parametric testing could proceed. Subsequently, we carry out a one-way ANOVA test to determine if there are differences in consumer shopping channel choices across the three scenarios. Following this, the least significant difference (LSD) method is employed for multiple comparisons. The results of these tests are summarized in

Table 3. Analyzed results.

Conditions	ANOVA Analysis		LSD Test
	F	Sig.	(I)	(J)	Sig.
When purchasing a fabric sofa with a shopping trip length of 5 km	5.996	0.003	ND	PD	0.019
				FD	0.001
When purchasing “one air conditioner” with a shopping trip length of 10 km	1.211	0.298	ND	PD	0.262
				FD	0.135

(Notes: ND—no disclosure; PD—partial disclosure; FD—full disclosure).

.

According to Table 3, when purchasing a fabric sofa with a shopping trip length of 5 km, the ANOVA test yields an F-value of 5.996, with a significance level of 0.003, significantly below the 0.05 threshold. This indicates a significant difference between the mean values of the three scenarios, showcasing considerable variation in consumers’ choices of shopping channels for “a fabric sofa” under scenarios with varying levels of information disclosure. The significance levels for the LSD mean differences stand at 0.019 and 0.001, respectively, well below the 0.05 significance level. This analysis robustly indicates that awareness of green consumption significantly influences the choice of shopping channels when consumers are purchasing fabric sofas.

When purchasing “one air conditioner” with a shopping trip length of 10 km, the ANOVA test reveals an F-value of 1.211, and the significance level is 0.298, which surpasses the 0.05 threshold. This result indicates that there is no statistically significant difference between the mean values across the three scenarios. Given that the ANOVA test results for “one air conditioner” are not significant, and the LSD data do not achieve a significant level, it can be concluded that there is no noticeable difference in consumer preferences for shopping channels when buying this product. This indicates that consumers may prioritize aspects such as quality, price, travel cost, and other factors over carbon emissions when selecting a shopping channel for air conditioners. In such instances, awareness of green consumption does not significantly influence the decision-making process, indicating that the impact of GCA on shopping channel choice is overshadowed by other, more pivotal factors.

In summary, while GCA can impact shopping channel choices in certain scenarios, this influence is not universally significant across all contexts.

4.3. The Impact of Disclosure of More Specific Carbon Emission Information on Shopping Channel Choice

4.3.1. Descriptive Analysis

To address the question “Does more specific carbon information disclosure matter?”, we select two specific scenarios for further examination: “purchasing a pair of sunglasses and a comb at 3 km” and “purchasing a shoebox at 0.5 km”. We compare the number of consumers opting for different shopping channels under conditions of partial disclosure against those under conditions of full disclosure. This comparison aims to observe how the extent of information disclosure influences consumer choices between shopping channels. The outcomes of this comparison are illustrated in Figure 5a,b.

Figure 5a,b showcase the impact of varying levels of carbon information disclosure on consumers’ shopping channel choices.

Figure 5a depicts the scenario of “purchasing a pair of sunglasses and a comb at 3 km”, where, with partial disclosure, the preference for the online shopping channel is noted. However, upon full disclosure of the carbon emission information, there is an evident shift towards the offline channel. Specifically, there is a notable increase in the number of consumers choosing the offline channel when provided with full disclosure, rising from 84 to 110. This significant uptick suggests that detailed information regarding the environmental impact effectively motivates consumers to select the shopping option associated with lower carbon emissions.

In the context of “purchasing a shoebox at 0.5 km”, illustrated in Figure 5b, a similar trend is observed. The shift from partial to full disclosure leads to a significant increase in the number of consumers opting for the offline shopping channel, rising from 115 to 132. Considering the data that “offline shopping results in a saving of 0.59 kg CO₂ per transaction and if 10,000 people switch to offline shopping for the shoebox, approximately 328 trees could be saved annually”, the move towards full disclosure could result in considerable environmental benefits.

The increase in offline shopping choices with full disclosure—illustrated by the higher bars in the figures—demonstrates a clearer understanding among consumers of the environmental benefits associated with this choice. This evidence strongly supports the statement that more comprehensive carbon information disclosure can indeed encourage consumers to prefer shopping channels with lower carbon emissions, reinforcing the role of informed decisions in fostering environmentally friendly consumer behavior. We will proceed to conduct further tests to determine the significance of the hypothesis.

4.3.2. Consistency Test

Continuing in the vein of Section 4.2.2, we proceed to further verify the statistical significance of the impact of carbon disclosure. After all scenarios successfully pass the chi-square test, an ANOVA analysis is conducted. The outcomes of these tests are concisely summarized in

Table 4. Analyzed results.

Conditions	ANOVA Analysis		LSD Test
	F	Sig.	(I)	(J)	Sig.
When purchasing “a pair of sunglasses and a comb” with a shopping trip length of 3 km	3.945	0.020	PD	FD	0.013
When purchasing “a shoebox” with a shopping trip length of 0.5 km	1.506	0.223	PD	FD	0.113

(Notes: PD—partial disclosure; FD—full disclosure).

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As shown in Table 4, when purchasing “a pair of sunglasses and a comb” with a shopping trip length of 3 km, the ANOVA test results in an F-value of 3.945, with a significance level of 0.020, which is below the 0.05 threshold. This indicates a statistically significant variation in consumers’ preferences for shopping channels under different scenarios of different levels of information disclosure. The LSD test further highlights a significant difference between the scenarios of partial disclosure and full disclosure, with a significance value of 0.013, substantially below the 0.05 significance level. These results suggest that providing consumers with more comprehensive information regarding the carbon emissions of different shopping channels can significantly influence their shopping channel choice under certain conditions.

When purchasing “a shoebox” with a shopping trip length of 0.5 km, the ANOVA test yields an F-value of 1.506, with a significance level of 0.223, which exceeds the 0.05 threshold. This outcome suggests that there is no statistically significant difference in consumers’ shopping channel choice behaviors for shoeboxes under scenarios with different levels of information disclosure. This finding is consistent with the results of the LSD test. Specifically, in the LSD test, the significance value of the differences between scenarios of partial disclosure and full disclosure is greater than 0.1. This indicates that providing consumers with advanced information about the high and low levels of carbon emissions associated with the shopping channel does not significantly influence consumers’ choice of shopping channels for a shoebox.

In summary, there are specific scenarios where additional disclosure of carbon emissions significantly influences consumer choices for certain goods. This suggests that further disclosure of carbon emission information can indeed have a positive effect on transferring green awareness into green shopping behavior. However, this effect is not universally applicable across all scenarios. This variability underscores the need for further exploration into which scenarios additional carbon information effectively guides consumers towards choosing shopping channels with lower carbon emissions.

4.4. The Influence of Goods Type on Shopping Channel Choice

In Section 4.2 and Section 4.3, we confirmed that the disclosure of low-carbon emission information can indeed transfer awareness of green consumption into green shopping channel choice behavior in certain scenarios, though not universally. Therefore, it becomes crucial to identify under which conditions these strategies significantly impact consumer behavior. Before exploring the effective scenarios for information disclosure, we must first ascertain whether the type of goods significantly influences the choice of shopping channel. If it does, we need to identify scenarios in which the type of goods does not overshadow the effect of GCA. If not, we may consider shopping trip length as the primary variable in different scenarios to assess the efficacy of information disclosure.

4.4.1. Descriptive Analysis

To determine whether the type of goods significantly affects the choice of shopping channel, we initially perform a descriptive analysis on the number of consumers choosing different shopping channels when purchasing various types of goods, under identical shopping trip lengths and carbon emission disclosure levels. We set up three sets of comparisons, varying the shopping trip lengths (0.5 km, 3 km, and 5 km) and the level of information disclosure. The numbers of consumers opting for different shopping channels under various scenarios are depicted in Figure 6.

According to Figure 6a–c, it is evident that the number of consumers choosing different shopping channels for various types of goods displays noticeable differences. We will proceed to conduct further tests to determine the significance of the hypothesis.

4.4.2. Consistency Test

To ascertain the statistical significance of the impact of types of goods on shopping channel choices, we apply an independent samples test to the three samples in Section 4.4.1 to facilitate a comparative analysis of the differences attributable to types of goods under uniform conditions. As indicated in

Table 5. Analyzed results.

Scenarios	Levine’s Test		T-Test
	F	Sig.	t	df	Sig.	MD	SED	95%
								LL	UL
ND, 0.5 km	189.806	0.000	−10.593	415.457	0.000	−0.426	0.040	−0.505	−0.347
FD, 3 km	31.821	0.000	4.425	455.958	0.000	0.200	0.045	0.111	0.289
ND, 5 km	33.860	0.000	7.016	454.000	0.000	0.309	0.044	0.222	0.395

(Notes: ND—no disclosure; FD—full disclosure; LL—lower limit; UL—upper limit).

, the significance value for Levine’s test for homogeneity of variances across all three datasets is 0.000, significantly below the significance threshold of 0.05. This finding implies that the variances within these three datasets are not equivalent, necessitating the selection of t-test results that do not assume homogeneity of variance to enhance the accuracy of the t-test analysis.

According to Table 5, for the scenario “under ND at 0.5 km, as depicted in Figure 6a”, the t-value is −10.593, the degrees of freedom (df) is 415.457, and the two-tailed significance (Sig. (two-tailed)) is 0.000, significantly below the 0.05 significance level. This indicates a substantial difference between the two samples regarding shopping channel choice for this scenario. The mean difference (MD) is 0.426, with a standard error of the difference (SED) of 0.040. The 95% confidence interval for the MD ranges from −0.505 to −0.347, a range that notably excludes zero, further corroborating the significant variance in samples and affirming the robustness of this difference at the 95% confidence level.

Similarly, for the analyses for the scenarios “under FD at 3 km, as depicted in Figure 6b” and “under ND at 5 km, as depicted in Figure 6c”, each shows a significance level of 0.000, also well below the 0.05 threshold. The differences and the confidence interval results for these scenarios similarly demonstrate a strong and significant distinction between the sample sets.

In summary, when the level of carbon disclosure and the shopping trip length remain constant, the variability in the type of goods significantly affects consumers’ choices of shopping channels, demonstrating statistical significance. This suggests that the variability of the goods being purchased is a crucial factor guiding consumers to greener shopping channels.

5. Discussions

As discussed in Section 4.1, awareness of green consumption indeed exists. Section 4.2.2 and Section 4.3.2 demonstrate that awareness of green consumption can contextually influence consumer choices and that varying levels of carbon emission information disclosure from different shopping channels can have different degrees of effect on transferring awareness of green consumption into green shopping channel choice behavior. This indicates that the strategic disclosure of information, highlighting the relative environmental advantages of each shopping alternative, has significant potential to leverage the GCA of consumers and lead to greener consumer behavior.

However, implementing such a policy immediately and indiscriminately may not be feasible, as emphasizing information comes with its own set of costs. Directly providing excessive information to consumers could lead to information overload, potentially diminishing the effectiveness of the guidance. Therefore, this paper leverages data from various scenarios to further analyze which scenarios are more conducive to providing consumers with information, which scenarios should emphasize the comparative carbon emission information of online versus offline shopping channels, and in which scenarios increased full disclosure will significantly enhance the direction of consumer shopping channel choice behavior.

5.1. Scenario Analysis

To elucidate the precise conditions under which the joint effect of GCA and carbon information disclosure exerts the greatest impact, and to ascertain when specific low-carbon information substantially sways consumer choices—without being overshadowed by variables such as shopping trip length (encompassing both travel time and cost) or the type of goods—we conducted analyses across 48 distinct scenarios. This analysis aimed to ascertain the proportion of shifts in shopping channel choice and the significance of these shifts across different information disclosure scenarios. This approach included the use of the LSD test to assess the corresponding changes in means, thereby facilitating a nuanced understanding of how various forms of information disclosure influence consumer shopping channel decisions.

The absolute value of the mean difference (indicative of the shift in shopping channel choice) and the corresponding significance under different scenarios are depicted in Figure 7, Figure 8, Figure 9 and Figure 10. Here, “N-P” and “N-F” denote comparisons between scenarios with no disclosure of low-carbon information versus partial disclosure and no disclosure versus full disclosure of information, respectively. These comparisons help identify scenarios in which the disclosure of information proves effective in transferring GCA into green shopping channel choice behavior. “P-F” represents the comparison between scenarios under partial versus full disclosure of carbon emission information, utilized to examine whether additional information disclosure makes GCA effective and not overshadowed by other factors, thereby leading more consumers to opt for green shopping channels with lower carbon emissions.

Figure 7 depicts scenarios within a 0.5 km shopping trip length. It displays the absolute values of mean differences and their significance across different goods categories when comparing no information (N-P, N-F) and partial to full information disclosure (P-F). Goods such as T-shirts and 2.5 kg apples show higher absolute mean differences, suggesting a significant shift in channel choice when low-carbon information is disclosed. The significance markers indicate that these shifts are statistically significant.

Figure 8 shows similar comparisons for a 3 km shopping trip length. Again, the categories such as T-shirts and tissue and trash bags indicate notable absolute mean differences, with the tissue and trash bags category showing high statistical significance, which means that the disclosure of information greatly influences consumer choice for these items.

Figure 9 presents the analysis for a 5 km shopping trip length, with goods like fabric sofas and 2.5 kg pork displaying considerable absolute mean differences. This suggests a substantial impact of information disclosure on shopping channel choice for these items, particularly for fabric sofas where the significance is also high.

Figure 10, pertaining to a 10 km shopping trip length, shows the analysis for products including air conditioners and 2.5 kg crab. In this scenario, the air conditioner category shows a significant absolute mean difference and high significance, indicating a strong effect of full disclosure on consumer shopping channel choice behavior.

Across all figures, the types of goods and the comparisons that show the most significant results indicate where the disclosure of low-carbon information has the most substantial impact on shifting consumer shopping channel choice. This evidence supports the use of one-way ANOVA and the LSD test for a nuanced understanding of the effects of low-carbon information disclosure.

5.2. Policy Recommendations

To foster environmentally conscious consumer behavior, policymakers and retailers should consider the following recommendations:

(1)

For types of goods demonstrating significant shifts in consumer choices with full information disclosure, such as 2.5 kg of apples (a type of fresh food), fabric sofas, and tissue and trash bags, it is recommended to implement an information disclosure policy. This strategy is most effective, particularly if the costs associated with comprehensive information disclosure are minimal. It encourages green consumption without overburdening the consumers with additional costs. When implementing an information disclosure policy, especially for goods like fabric sofas with significant impacts over longer trips (e.g., 5 km, as shown in Figure 9), it is crucial to account for consumer behavior differences across trip lengths. This variation should inform both theoretical models and practical policy design.

(2)

It is imperative to evaluate the costs of low-carbon information dissemination against the potential benefits. If the expense of providing full disclosure is on par with that of partial disclosure, then full disclosure should be prioritized to maximize the environmental benefits. However, care must be taken to avoid consumer memory fatigue, which can result from an overload of information. It is imperative to evaluate the costs of low-carbon information dissemination against the potential benefits. If the expense of providing full disclosure is on par with that of partial disclosure, then full disclosure should be prioritized to maximize environmental benefits. However, care must be taken to avoid consumer memory fatigue, which can result from an overload of information. In practical application scenarios, providing too much information at once may lead to consumers lacking the patience or time to review all the details. Therefore, when the impact of providing full information on shopping choices is less significant compared with partial information, it might be more effective to offer only partial information. Thus, a balance must be struck between the quantity and quality of the information provided. In longer shopping trips like the 10 km trip in Figure 10, where air conditioners show significant differences, the cost–benefit analysis should consider the travel distance, as it affects the effectiveness of information strategies. Integrating factors like trip length is crucial for both theoretical models and practical low-carbon policies.

(3)

We need to tailor disclosure based on significance. In cases where both N-P and N-F comparisons are significant, indicating that both partial and full disclosures influence consumer choices, a targeted approach may be more efficient. When the cost of full disclosure is substantially higher, partial disclosure could be prioritized. This ensures that consumers receive vital information without the potential adverse effects of memory fatigue, which may arise with full disclosure. As shown by the significance of tissue and trash bags in Figure 8 and T-shirts in Figure 7 and Figure 8, disclosure should be tailored by product type and shopping distance. This nuanced approach advances consumer choice theory and provides practical guidelines for optimizing environmental messaging without overwhelming consumers.

(4)

If high levels of disclosure potentially lead to memory fatigue, efforts should be made to enhance consumers’ retention of information. This could involve the strategic placement of information, the use of memorable visuals, or the integration of reminders in the shopping environment. Such measures can make the information more accessible and less taxing on consumer memory, thereby supporting informed and environmentally friendly decision making.

In conclusion, the goal of these policy recommendations is to optimize the dissemination of low-carbon information to support green consumption while considering the practical aspects of consumer information processing and the associated costs. These strategies should be flexible, adaptable to different shopping trip lengths, and sensitive to the varying impacts on different goods categories.

5.3. Summary

This study extends the existing literature by addressing several research gaps identified in the review. First, while previous studies have acknowledged the differences in carbon emissions across various shopping channels, there has been limited focus on how GCA can be leveraged to guide consumers towards more eco-friendly shopping choices. Our research contributes by demonstrating that targeted information disclosure can effectively translate green awareness into actionable green behaviors, particularly in scenarios involving high-impact goods like tissue and trash bags during a 3 km shopping trip.

Second, we build on the established understanding of the factors influencing shopping channel choices, such as price and delivery time, by incorporating GCA as a critical variable. Our findings validate that this awareness, when coupled with specific carbon emission data, significantly influences consumer decisions, filling a notable gap in the literature regarding the contextual application of green awareness.

Lastly, our work adds to the ongoing discussion on the consistency and effectiveness of green awareness by showing that varying levels of carbon information disclosure do not uniformly impact all consumers. Instead, the effectiveness of these disclosures varies across scenarios, which underscores the importance of context-specific strategies in promoting green shopping behaviors. This nuanced understanding enhances the current body of knowledge and provides practical insights for policymakers aiming to advance the global environmental agenda through informed consumer behavior.

6. Conclusions

The comprehensive assessment of the influence of GCA and carbon information disclosure on green shopping channel choice behavior, based on SP survey data, offers a multi-faceted view of consumer behavior. The analysis offers key insights into the nuances of how information disclosure can translate psychological perception—specifically GCA, as discussed in this paper—into green shopping channel choice behavior. The findings of this paper are as follows:

(1)

While GCA can influence shopping channel choice behavior in certain scenarios, guiding consumers towards greener options, this influence is not universally significant across all contexts. Specifically, the impact of awareness is found to be more pronounced in scenarios where the disclosure of carbon information is more comprehensive.

(2)

The additional details provided in full disclosure scenarios, particularly those that include specific data on carbon emissions, have been found to more effectively guide consumers towards green shopping channels with lower carbon emissions in some cases. However, this is not consistent across all scenarios; in some instances, the effect of additional disclosure did not reach statistical significance.

(3)

The type of goods significantly affects shopping channel choices under identical shopping trip lengths and disclosure scenarios. This suggests that, for certain goods, the propensity to choose low-carbon emission channels is inherently stronger, which can sometimes overshadow the influence of GCA.

(4)

Delving deeper into effective disclosure scenarios, our analysis of 48 different scenarios revealed that the effect of disclosing the information is pivotal, especially for products such as fresh food, where targeted information seems to have a substantial effect on guiding consumer choices towards more sustainable options.

(5)

The policy recommendations derived from this research advocate for a nuanced approach to information disclosure, one that takes into account the potential for information overload and the specific nature of the goods involved. Strategies should be tailored to transfer green awareness from psychological perception to green shopping channel choice behavior by enhancing the retention of disclosed information. It emphasizes that effective disclosure is contingent upon a deep understanding of the conditional factors that influence shopping choices and the recognition that a one-size-fits-all approach is less likely to be successful. By acknowledging and responding to these intricacies, policymakers and businesses can better foster a marketplace where green consumption is not just an awareness but an actionable choice for consumers.

By acknowledging and responding to these intricacies, policymakers can craft targeted policies that enhance the effectiveness of information disclosure, ensuring that consumers are better equipped to make sustainable choices. Retailers can support this effort by providing clear, low-carbon options through packaging and product labeling, making the environmental impact of each purchase transparent. This collaborative approach will help transform green consumption from mere awareness into actionable behavior, reinforcing the positive impact of individual choices on reducing carbon emissions and fostering a more sustainable marketplace.

One limitation of our study is that it primarily focuses on the impact of carbon emissions on shopping choices, without extensively considering other influential factors such as the perishability of goods or the convenience of store delivery versus consumer-arranged transportation. While these aspects could significantly affect consumer behavior, they were beyond the scope of our research. Another limitation is that our study examines the behavioral aspects of consumers’ choices between online and offline shopping channels, without thoroughly investigating the reasons behind these choices. For instance, factors such as the ability to personally select fresh produce or the trust consumers place in local markets due to their familiarity with the source and quality of the products are not deeply explored in our analysis. Future research could address these additional factors to provide a more comprehensive understanding of consumer decision making in the context of sustainability.