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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 4: 1083 - 1093

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.04.026

Tourism Resources and Its Integration with Cultural and Creative Industries

Exploring Tourists’ Low-carbon Cognition and Influencing Factors from the Dimension of Education Level

  • CHENG Jinhong , *
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  • College of History and Tourism Culture, Shanxi Normal University, Taiyuan 030031, China
* CHENG Jinhong, E-mail:

Received date: 2023-02-20

  Accepted date: 2024-02-15

  Online published: 2024-07-25

Supported by

Program for the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi(2023W064)

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Abstract

The cognition of low-carbon tourism among tourists is closely related to education level. In this study, the degree of coordination of low-carbon cognition with different educational levels is assessed by the coupling model in Wutai Mountain, and the effect of each factor on low-carbon cognition is analyzed by the geographical detector. The results show that: (1) The six cognition aspects of low-carbon tourism gradually transition from the level of intermediate coordination to good coordination with the advancement of the education level. Both the low-level and lower-level tourists belong to the lag type of low-carbon visiting cognition, and the higher-level tourists belong to the lag type of low-carbon shopping cognition, while the high-level tourists show the lag type of low-carbon food cognition. (2) According to the individual factors and interactive effects in the geographical detector, each impacting factor has a decisive effect on tourists’ cognition of low-carbon tourism, and the effect of any two factors after interaction shows either a double-factor or nonlinear enhancement. The findings of this study provide valuable practical implications for helping tourism destinations to educate tourists and improve their low-carbon tourism options. At the same time, this study will provide theoretical standards for measuring tourists’ cognition of low-carbon tourism, so as to enrich and improve the theoretical research related to low-carbon tourism.

Key words： low-carbon tourism; cognitive differences; education level; model of coupling coordination degree; Geo-detector

Cite this article

CHENG Jinhong . Exploring Tourists’ Low-carbon Cognition and Influencing Factors from the Dimension of Education Level[J]. Journal of Resources and Ecology, 2024 , 15(4) : 1083 -1093 . DOI: 10.5814/j.issn.1674-764x.2024.04.026

1 Introduction

With the increasing severity of climate change, changing the public’s concept of environmental protection and consumption patterns is particularly critical (Burke et al., 2017; Weaver and Miller, 2019; Esteve-Llorens et al., 2020; Grazzini et al., 2020). Tourism is closely related to climate (Amelung and Moreno, 2012; Amengual et al., 2012). The carbon emissions derived from tourism are responsible for 5% of global climate warming (Scott et al., 2007), indicating that the low-carbon transformation of tourism is imperative. The key to developing low-carbon tourism is to improve tourists’ awareness of low-carbon options.
Existing research suggests that tourists have a certain willingness to participate in low-carbon tourism consumption (McKercher et al., 2010; Skamp et al., 2013), but their willingness drops when it comes to taking concrete action (Juvan and Dolnicar, 2014). This indicates that tourists’ cognition of low-carbon tourism remains at the concept understanding level, which needs further improvement at the activity level (Rosenbloom, 2017). Low-carbon tourism is a comprehensive tourism activity involving food, housing, transportation, tourism, shopping, entertainment and other links. Currently, implementing low-carbon education in a targeted way is urgently needed to promote better low-carbon actions (Koo et al., 2014; Frumhoff et al., 2015).
Tourists’ cognition of low-carbon tourism is not only related to their demographic characteristics and subjective attitude (Bogicevic et al., 2018; Zhang and Zhang, 2020), but is also related to external factors, such as government policies, social norms and the low-carbon environments of tourist destinations (Tanford and Montgomery, 2014; Lin and Hemmington, 1997). Demographic characteristics are the basic factors that affect tourists’ cognition of low-carbon tourism. As an important factor of demography, education level is the key to affecting the cognition of low-carbon tourism in theory. Existing research suggests that the higher the education level, the more tourists can realize the benefits of low-carbon tourism to the environment, and the more willing they are to implement low-carbon tourism behavior (Tervo-Kankare et al., 2013; Hindley and Font, 2014). At present, there are still no reports on the effect of education level on tourists’ low-carbon cognition in different links or the mechanisms driving it. For the practical needs of low-carbon tourism, the relationship between tourists’ education level and their low-carbon cognition in different links remains to be analyzed.
Considering the shortcomings of existing studies, this study examines the cognitive differences and impact mechanisms of tourists in different low-carbon tourism links as related to the level of education. The coupling coordination degree model was used to study the coordination of differentially educated tourists’ cognition of low-carbon tourism. The effects of various factors on low-carbon cognition were analyzed by using the Geo-detector.

2 Literature review

Tourism cognition is a tourist’s evaluation of the comprehensive environment of a tourist destination (Galloway et al., 2008; Bhochhibhoya et al., 2019). This cognition will lead to the formation of tourists’ relevant value systems, and the formation of tourist satisfaction will also be affected by the level of tourists’ social cognition (Becken et al., 2003; Burgess et al., 2016). Low-carbon cognition is the basis for the formation of low-carbon attitudes and behavior (Mair, 2011; Eijgelaar et al., 2016). A high level of low-carbon cognition makes it easy to promote the right low-carbon attitude, and subsequently a positive low-carbon behavior (Seetaram et al., 2018). Therefore, identifying tourists’ low-carbon cognition levels can help to guide low-carbon behaviors.
However, the factors influencing the formation of tourists’ tourism cognition are complex and multifaceted. Both internal characteristics and external factors will affect tourists’ low-carbon cognition. Some studies have explained how various demographic factors affect tourists’ awareness of low-carbon tourism (Cannon and Ford, 2002; Kim, 2012; Lu et al., 2016; Liu and Suk, 2021). In terms of gender, male tourists have a higher awareness of low-carbon tourism than female tourists, mostly because males are more open-minded and more easily exposed to low-carbon tourism as an emerging concept (Rasoolimanesh et al., 2020). In terms of age, young or middle-aged tourists have a superior awareness of low-carbon tourism, because they are more inclined to try new low-carbon tourism options, while older tourists are less sensitive to low-carbon tourism and prefer traditional tourism modes. In terms of occupation, most teachers, students, civil servants, tourism practitioners and some company employees have higher levels of awareness of low-carbon tourism than tourists who work in other occupations (Leonidou et al., 2015). In terms of income, high-income tourists are more willing to participate in low-carbon tourism as they are more capable of purchasing low-carbon and energy-saving products. In addition to these, other factors such as education level and information acceptance level have an important effect on tourists’ cognition (Atzori et al., 2019).
However, studies on low-carbon cognition from the perspective of education level are scarce. Only a few scholars have investigated the role of educational experience in improving energy literacy and behavior (Hu et al., 2013; Horng et al., 2013, 2014; Teng et al., 2014). Most scholars have suggested that the establishment of literacy is a prerequisite for better, faster and more effective low-carbon emission reduction, which contributes to the public’s benign understanding of carbon emission reduction. Therefore, this study fills this knowledge gap by analyzing tourists’ cognition of low-carbon tourism activities in relation to different education levels, so as to promote the development of low-carbon tourism.

3 Methodology

3.1 Research area

Mount Wutai is located between 38°55'-39°05'N and 113°29'-113°39'E, in Shanxi Province. It is one of the four most famous Buddhist mountains, one of the top ten famous mountains, and one of the national 5A tourist attractions. It was listed as a World Cultural Heritage Site in June 2009. Mount Wutai is not only the largest ancient Buddhist complex in the world, but it also has a very good ecological environment. With rich and unique tourism resources, Mount Wutai is a tourist destination that integrates religious culture and ecological sightseeing, thereby promoting its status as a summer resort and a good place for a leisure vacation. In 2017, the number of tourists in the Wutai Mountain scenic area reached 5.686 million, and tourist income reached 7.2 billion yuan. However, the large numbers of tourists and activities led to a large amount of carbon emissions (220000 t yr-1), and the carbon absorption capacity of the ecological system is only about 80000 t yr-1. The Mount Wutai scenic area has been a serious carbon source, which means that the imminent implementation of low-carbon tourism is necessary.

3.2 Data collection

The data collection was carried out via a questionnaire survey. A total of 1100 questionnaires were handed out to people who visited Mount Wutai scenic area, with 1021 completed questionnaires received, resulting in an effective rate of 92.8%. In general, the number of samples should be no less than 10-15 times the number of variables (Chou and Bentler, 2002). The Cronbach’s alpha coefficients of all dimensions ranged from 0.862 to 0.918, which indicated that the questionnaire had good reliability.
The questionnaire was developed to measure the cognition of low-carbon tourism and the factors impacting it. The questionnaire mainly included three sections. The first section surveyed six aspects of tourists’ low-carbon tourism cognition by using 24 items. The six aspects are cognition of low-carbon transport, cognition of low-carbon shopping, cognition of low-carbon accommodation, cognition of low-carbon visiting, cognition of low-carbon circulation and cognition of low-carbon food. The second section assessed nine factors which impact the cognition of low-carbon tourism with 36 items. The nine impacting factors are cognition of carbon reduction, cognition of carbon emissions, man-land values, individual-social responsibility, individual professional skills, cognition of low-carbon tourism costs, social reference standards, perception of institutional restriction and cognition of external conditions (Cheng et al., 2018). The five-point Likert scale was used to score each survey item. The last section collected demographic information, including education level, sex, age, occupation, income, and so on.
Tourists were divided into four groups according to their education level. The low-level type consists of tourists with an education level of junior high school or below, accounting for 12.6% of the total sample (129). The mid-low-level type consists of tourists with an education degree of high school or technical school, accounting for 29.3% of the total sample (299). The mid-high-level type consists of tourists with an associate’s or bachelor’s degree, and made up 52.9% of sample (540). The high-level type consists of tourists with a graduate degree, accounting for 5.2% of the total sample (53).

3.3 Coupling coordination degree model

The coupling coordination degree model is a method for measuring the level of mutual promotion and restriction between systems or various elements in the development process (Li et al., 2012). This study applied the coupling model to compare the level of coordination between tourist groups with different education levels on the six cognition aspects of low-carbon tourism. The process followed Guo et al.’s (2018) method to conduct a four-step analysis.
First of all, the range method was used to normalize the original values of the indexes for eliminating the dimensional differences. The formula can be expressed as:
${{u}_{ij}}=\left\{ \begin{matrix} \frac{{{x}_{ij}}-\min ({{x}_{ij}})}{\max ({{x}_{ij}})-\min ({{x}_{ij}})}\begin{matrix}, & {{x}_{ij}}\text{ is a positive indicator} \\ \end{matrix} \\ \frac{\max ({{x}_{ij}})-{{x}_{ij}}}{\max ({{x}_{ij}})-\min ({{x}_{ij}})}\begin{matrix}, & {{x}_{ij}}\text{ is a negative indicator } \\ \end{matrix} \\ \end{matrix} \right.$
In formula (1), uij represents the normalized value of index xij, xij is the j index of the i elements (i=1, 2, 3, 4, 5, 6; j=1, 2,$\cdots $, n), and max(xij) and min(xij) are respectively the maximum and minimum values of index xij.
Secondly, the comprehensive evaluation model of the six cognition aspects in low-carbon tourism can be respectively expressed as follows:
${{U}_{i}}=\sum\limits_{j=1}^{n}{{{\lambda }_{ij}}{{u}_{ij}}}\begin{matrix}, & i=1,2,3,4,5,6 \\ \end{matrix}$
In formula (2), U1, U2, U3, U4, U5, and U6 represent the comprehensive evaluation functions on the cognition of low-carbon transport, cognition of low-carbon shopping, cognition of low-carbon accommodation, cognition of low-carbon visiting, cognition of low-carbon circulation and cognition of low-carbon food, respectively. The weight of each index is λij, which can be obtained by the entropy weight method as shown in Table 1. The variable n is the total number of indicators.
Table 1 The index system and weights of low-carbon tourism cognition
First-class index Weight of first-class index Second-class index Weight of
second-class index
Cognition of low-carbon transport 0.0549 Cars emit a lot of carbon and are bad for the environment 0.2624
The best way to carry out low-carbon tourism is on foot 0.2488
Strengthen training and publicity on carbon reduction 0.1881
Build a public transportation system to protect the environment 0.1553
Vigorously promote the development of scenic public transport 0.1454
Cognition of low-carbon shopping 0.1148 Willing to pay extra fees to support low-carbon shopping 0.3647
Support the purchase of local products 0.3019
Products with a low-carbon label are preferred 0.1269
Willing to choose a low-carbon green hotel 0.1036
Willing to choose a restaurant with green ecological certification 0.1029
Cognition of low-carbon accommodation 0.1252 Air-conditioning temperature in hotels should be set according to national standards 0.5597
Hotels should use energy-saving lighting control systems 0.2257
Hotels should use water-saving toilets 0.2146
Cognition of low-carbon visiting 0.2578 Low-carbon tourism has nothing to do with protecting the ecological environment 0.4141
There are no carbon emissions from recreation 0.3156
There are no carbon emissions from garbage 0.2703
Cognition of low-carbon circulation 0.2221 Hotels should not use disposable items 0.4695
Hotel sheets and towels should be changed as needed 0.4019
Hotels should set up waste-water recycling systems 0.1286
Cognition of low-carbon food 0.2252 Carbon emissions of meat-eaters are higher than vegetarians 0.3594
Eating local ingredients in season will contribute to low-carbon environmental protection 0.3387
Carbon emissions between different level of hotels are not the same 0.3019
Thirdly, the models for the coupling degree and coupling coordination degree were developed based on the following formulas:
$C=6\times {{\left[ \frac{{{U}_{1}}\times {{U}_{2}}\times {{U}_{3}}\times {{U}_{4}}\times {{U}_{5}}\times {{U}_{6}}}{{{({{U}_{1}}+{{U}_{2}}+{{U}_{3}}+{{U}_{4}}+{{U}_{5}}+{{U}_{6}})}^{6}}} \right]}^{1/6}}$
$D=\sqrt{C\times T},\begin{matrix} {} \\ \end{matrix}T=\frac{{{U}_{1}}+{{U}_{2}}+{{U}_{3}}+{{U}_{4}}+{{U}_{5}}+{{U}_{6}}}{6}$
where C means the coupling degree in formula (3), while D means the coupling coordination degree, and T means the coordination index based on the six cognition aspects of low-carbon tourism in formula (4). The coupled model reflects the intensity of the six low-carbon cognitive interactions, while the coupled coordination model reflects the degree of coordination.
Lastly, in order to reflect the coordination degree of all aspects of low-carbon tourism cognition, Tang’s (2015) method was adopted to divide the intervals and coordination levels of the coupling coordination degree into 10 categories (Table 2).
Table 2 The standards for evaluating the coupling coordination degree
Coupling coordination degree interval Coordination degree Coupling coordination degree interval Coordination degree
0<D≤0.1 Severe maladjustment 0.5<D≤0.6 Forced coordination
0.1<D≤0.2 Serious maladjustment 0.6<D≤0.7 Slight coordination
0.2<D≤0.3 Moderate maladjustment 0.7<D≤0.8 Intermediate coordination
0.3<D≤0.4 Mild maladjustment 0.8<D≤0.9 Good coordination
0.4<D≤0.5 On the verge of maladjustment 0.9<D≤1 High-quality coordination

3.4 Geo-detector method

The Geo-detector is commonly used to detect spatial differentiation and reveal the main driving forces (Wang and Xu, 2017). In this study, the methods of factor detection and interaction detection were selected to study the effects of the nine impacting factors on low-carbon cognition of tourists with different education levels.

3.4.1 Factor detection

Factor detection is used to determine the spatial differentiation of the dependent variables and the explanatory power of the independent variable to the dependent variable, which is expressed by the q-value.
$q=1-\frac{\sum_{h=1}^{L} N_{h} \sigma_{h}^{2}}{N \sigma^{2}}=1-\frac{S S W}{S S T}, \quad S S W=\sum_{h=1}^{L} N_{h} \sigma_{h}^{2}, \quad S S T=N \sigma^{2}$
In formula (5), q is the influence of a factor with a range of [0,1]. The larger the value of q, the stronger the explanatory power of the independent variable X to attribute Y. Parameter h=1,…, L is the stratification of variable Y or X; Nh and N are the unit numbers of the layer h and all cognition of low-carbon tourism, respectively; σh2 and σ2 are the variances of Y values in layer h and the overall cognition, respectively (Fu et al., 2022); the SSW and SST are the sums of intralayer variances and total variance of the whole cognition of low-carbon tourism, respectively.

3.4.2 Interaction detection

Interaction detection is used to assess the functions between impacting factors, that is, it can forecast the explanatory power of dependent variables. The interaction type between any two factors Xi and Xj can be summarized as (Li et al., 2021):
If q(Xi∩Xj)<min(q(Xi),q(Xj)), then the interaction is weak and nonlinear;
If min(q(Xi), q(Xj))<q(Xi∩Xj)<max(q(Xi), q(Xj)), then the interaction is weak and univariate;
If q(Xi∩Xj)>max(q(Xi), q(Xj)), then the interaction is enhanced and bivariate;
If q(Xi∩Xj)=q(Xi)+q(Xj), then the two factors interact independently;
If q(Xi∩Xj)>q(Xi)+q(Xj), then the interaction is nonlinear enhanced.
In the calculation, Xi and Xj represent any two of the nine influencing factors.

4 Results

4.1 The comprehensive level of low-carbon tourism cognition

As shown in Table 3, the coupling model was used to calculate the comprehensive function values of U1, U2, U3, U4, U5, and U6, as well as the coupling and coordination degrees for the six cognition aspects of low-carbon tourism with different education levels.
Table 3 The coupling coordination degree and level of cognition system for low-carbon tourism.
Education level U1 U2 U3 U4 U5 U6 C D Coordination level Lag type
Low 0.750 0.625 0.707 0.603 0.613 0.629 0.960 0.789 Intermediate coordination Visiting cognition
Mid-Low 0.791 0.654 0.751 0.629 0.648 0.661 0.968 0.815 Good coordination Visiting cognition
Mid-High 0.808 0.645 0.770 0.656 0.667 0.672 0.967 0.820 Good coordination Shopping cognition
High 0.804 0.744 0.811 0.707 0.755 0.687 0.982 0.858 Good coordination Food cognition

Note: U1, U2, U3, U4, U5, and U6 represent the comprehensive evaluation functions of cognition of low-carbon transport, cognition of low-carbon shopping, cognition of low-carbon accommodation, cognition of low-carbon visiting, cognition of low-carbon circulation, and cognition of low-carbon food, respectively. C means the coupling degree and D means the degree of coupling coordination.

The results reveal that the comprehensive development levels for the cognition of low-carbon transport and accommodation were highest among the six cognition aspects, while the development level of the cognition of low-carbon visiting was the lowest. In general, the higher the education level, the higher the comprehensive development level of cognition of the tourists (Wu et al., 2017).
Specifically, the comprehensive development level of the cognition of low-carbon transport (U1) was between 0.750 and 0.808. Tourists with low education levels showed the lowest cognition, while those with mid-high levels showed the highest cognition. This illustrates that with an advanced level of education, tourists can more easily recognize traffic carbon emissions, so it is also easier for them to carry out low-carbon transport. The comprehensive development level of tourists’ cognition of low-carbon shopping (U2) varied from 0.625 to 0.744. Tourists with low education levels showed the lowest cognition, while those with high education levels showed the highest cognition. Note that the cognition of low-carbon shopping among tourists with mid-high education levels (0.645) was lower than for those with mid-low levels of education (0.654). The reason is that tourists with mid-high education levels were mostly students who did not have enough income to support low-carbon shopping. The comprehensive development level of tourists’ cognition of low-carbon accommodation (U3) ranged from 0.707 to 0.811. Tourists with low education levels showed the lowest cognition, while those with high education levels showed the highest cognition.
The comprehensive development levels of tourists’ cognition of low-carbon visiting (U4), low-carbon circulation (U5), and low-carbon food (U6) were comparatively lower. Tourists with low education levels showed the lowest cognition for these three aspects, while those with high education levels showed the highest cognition. The main reason is that tourists with education levels of junior high school or below were unable to relate their recreational activities with carbon emissions or understand that garbage can cause climate warming. On the other hand, tourists with high education levels were not only better educated but also had good economic and social status. Therefore, they are able to realize that tourism is not a smokeless industry and are willing to practice low-carbon behaviors, such as paying extra for carbon offsets (Emberger-Klein and Menrad, 2018).

4.2 Analysis of the coupling degree and the coordination degree

With an improvement in tourists’ education level, the coupling degrees of the six cognition aspects of low-carbon tourism showed slow-growth trends (Table 3), and the values were all greater than 0.96, which is considered high. This shows that the six aspects of tourists’ low-carbon cognition were closely linked and mutually promoting.
The coupling coordination degree values of low-carbon tourism cognition for the education levels were all greater than 0.7. Based on the 10-class criteria, with tourists’ education level advancing from low to high, the coupling coordination degree values of the six cognition aspects improved from intermediate to good coordination. Specifically, the coupling coordination degree for tourists with low education levels was 0.789, which belongs to the intermediate coordination class. Comparing the six cognition aspects, cognition of low-carbon transport had the highest value, while cognition of low-carbon visiting had the lowest value. This indicates that cognition of low-carbon transport had a stronger feedback effect on tourists with low education levels, while the feedback effect of low-carbon visiting on cognition was weaker. The coupling coordination degree for tourists with mid-low education levels was greater than 0.8, upgrading it to a good coordination class. Similar to the cognition of tourists with low education levels, in this group of tourists, cognition of low-carbon transport had the highest value, while cognition of low-carbon visiting had the lowest value. This shows that the cognition of transport was the main driver for tourists with mid-low education levels, while the cognition of low-carbon visiting was still the main obstacle.
The coupling coordination degree for tourists with mid-high education levels was 0.820, which also belongs to the good coordination class. The cognition of low-carbon transport showed the highest value, while the cognition of low-carbon shopping showed the lowest value in this group. This indicates that the cognition of low-carbon transport had a greater influence on tourists with mid-high education levels, while the impact on the cognition of low-carbon shopping was much weaker. Compared to tourists with low and mid-low education levels, the cognition of low-carbon visiting was greatly improved in this group. The coupling coordination degree for tourists with high education levels was 0.858, which also belongs to the good coordination class. The cognition of low-carbon accommodation showed the highest value, while the cognition of low-carbon food was the lowest in the group. This suggests that the cognition of low-carbon accommodation has a strong feedback effect on tourists with high education levels, while the cognition of low-carbon food has a weaker feedback effect. Therefore, to improve the coordination level of low-carbon cognition overall, it is necessary to formulate targeted low-carbon education for tourists with different education levels.

4.3 Factor detection analysis of low-carbon tourism cognition

In order to assess the cognitive status of low-carbon tourism among tourists with different educational levels, factor and interaction detection of the Geo-detector were used to determine the significance of factors and evaluate the explanatory power and degree of interaction among the factors. The nine indicators were included in both analyses as factors influencing tourists’ low-carbon tourism cognition, namely awareness of carbon reduction (X1), cognition of carbon emissions (X2), man-land values (X3), individual-social responsibility (X4), individual professional skills (X5), cognition of low-carbon tourism costs (X6), social reference standards (X7), perception of institutional restriction (X8), and cognition of external conditions (X9).
The factor detection results (Table 4) revealed that the q-value of each detection factor passed the test at a threshold level of 5% (Wang and Xu, 2017), indicating that the nine selected detection factors all had significant effects on determining tourists’ low-carbon tourism cognition.
Table 4 The q-value statistics of cognitive impact factors of low-carbon tourism
Education level Impacting factor X1 X2 X3 X4 X5 X6 X7 X8 X9
Low q-value 0.172 0.097 0.070 0.025 0.053 0.036 0.134 0.155 0.045
P-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Mid-Low q-value 0.152 0.111 0.039 0.122 0.059 0.101 0.063 0.099 0.078
P-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Mid-High q-value 0.129 0.114 0.041 0.075 0.031 0.092 0.057 0.098 0.086
P-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
High q-value 0.158 0.064 0.148 0.086 0.065 0.039 0.232 0.127 0.183
P-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Note: X1 represents awareness of carbon reduction; X2 represents cognition of carbon emissions; X3 represents man-land values; X4 represents individual-social responsibility; X5 represents individual professional skills; X6 represents cognition of low-carbon tourism costs; X7 represents social reference standards; X8 represents perception of institutional restriction, and X9 represents cognition of external conditions.

According to the q-values, for tourists with low education levels, the awareness of carbon reduction (X1) was the leading factor impacting their low-carbon tourism cognition, with an explanatory power of 17.2%. The perception of institutional restriction (X8) was the second most influential factor, with an explanatory power of 15.5%. Individual-social responsibility (X4) was the weakest influencing factor, with an explanatory power of 2.5%. Due to their low education levels, this group of tourists did not know much about carbon emissions, which led to their weak awareness of carbon reduction. The form of their low-carbon tourism cognition was driven mainly by external factors, such as institutional and social constraints, so we should strive to seek a joint effect of internal and external factors.
For tourists with mid-low education levels, the awareness of carbon reduction (X1) was still the leading factor impacting their low-carbon tourism cognition, with an explanatory power of 15.2%. Compared to the tourists with low education levels, the q-value of individual-social responsibility (X4) was greatly improved to become the second most influential factor, with an explanatory power of 12.2%. The man-land values (X3) had the weakest effect on their low-carbon tourism cognition, with an explanatory power of 3.9%. This group of tourists showed enhanced social responsibility and had a certain consciousness, so they were no longer dependent mainly on external conditions. However, their subjective awareness of carbon reduction was still not high, and their individual-social responsibility varied greatly. Thus, the role of internal factors should be the focus of development for this group.
For tourists with mid-high education levels, the main driving force of their low-carbon tourism cognition was still an awareness of carbon reduction (X1), with an explanatory power of 12.9%. The second most influential factor was cognition of carbon emissions (X2), with an explanatory power of 11.4%, while the individual professional skills (X5) showed the smallest effect on their low-carbon tourism cognition, with an explanatory power of 3.1%. Compared with the previous two groups, this group of tourists showed increased awareness of carbon reduction. However, their cognition of carbon emissions lagged behind awareness, as they did not understand the specific mechanism of carbon emissions and the ways to reduce emissions. Thus, we should be most concerned about the role of internal factors.
For tourists with high education levels, the social reference standards (X7) was the leading factor impacting their low-carbon tourism cognition, with an explanatory power of 23.2%. The cognition of external conditions (X9) was the second most influential factor, with an explanatory power of 18.3%, while the cognition of low-carbon tourism costs (X6) showed the smallest effect on low-carbon tourism cognition, with an explanatory power of 3.9%. Unlike the tourists with first three education levels, this group of tourists had a high awareness of carbon reduction. However, they had no channels for participating in low-carbon behavior due to the lack of external conditions for low-carbon activities. Therefore, the improvement of external factors will greatly promote their low-carbon tourism behavior.

4.4 Interaction detection analysis of low-carbon tourism cognition

Table 5 shows the interaction detection analysis results, which indicate that the influences of factors on tourists’ low-carbon tourism cognition were not independent but could be enhanced through interactions. As shown in Table 5, for tourists with low education levels, the q-values of the nine impacting factors satisfied any q (X1 Ո X2) > q (X1) + q (X2), indicating that the influence of any two factors after the interaction was nonlinearly enhanced.
Table 5 The interactive detection statistics of the factors influencing low-carbon tourism cognition
Education level X1 X2 X3 X4 X5 X6 X7 X8 X9
Low X1 0.172
X2 0.372 0.097
X3 0.361 0.344 0.070
X4 0.306 0.291 0.204 0.025
X5 0.320 0.279 0.267 0.224 0.053
X6 0.328 0.313 0.303 0.218 0.217 0.036
X7 0.381 0.408 0.367 0.294 0.341 0.403 0.134
X8 0.406 0.424 0.391 0.273 0.278 0.308 0.320 0.155
X9 0.365 0.188 0.323 0.209 0.213 0.237 0.303 0.264 0.045
Mid-Low X1 0.152
X2 0.257 0.111
X3 0.214 0.178 0.039
X4 0.271 0.306 0.260 0.122
X5 0.251 0.197 0.168 0.249 0.059
X6 0.273 0.295 0.202 0.364 0.247 0.101
X7 0.300 0.240 0.221 0.321 0.159 0.249 0.063
X8 0.285 0.222 0.210 0.311 0.196 0.271 0.229 0.099
X9 0.254 0.238 0.237 0.253 0.250 0.303 0.224 0.223 0.078
Mid-High X1 0.129
X2 0.208 0.114
X3 0.177 0.207 0.041
X4 0.212 0.205 0.148 0.075
X5 0.195 0.166 0.108 0.142 0.031
X6 0.214 0.219 0.164 0.172 0.142 0.092
X7 0.215 0.198 0.149 0.156 0.120 0.160 0.057
X8 0.213 0.212 0.159 0.187 0.141 0.211 0.173 0.098
X9 0.223 0.210 0.154 0.175 0.139 0.185 0.155 0.183 0.086
High X1 0.158
X2 0.365 0.064
X3 0.479 0.352 0.148
X4 0.315 0.331 0.351 0.086
X5 0.383 0.373 0.433 0.231 0.065
X6 0.507 0.462 0.327 0.326 0.146 0.039
X7 0.395 0.486 0.550 0.384 0.383 0.387 0.232
X8 0.335 0.199 0.453 0.343 0.311 0.257 0.446 0.127
X9 0.365 0.288 0.488 0.353 0.467 0.325 0.505 0.310 0.183
For tourists with mid-low education levels, the q-values of (X1 Ո X2) and (X1 Ո X4) were greater than the maximum q-value of a factor. This suggests that awareness of carbon reduction interacted with the cognition of carbon emissions and individual-social responsibility, resulting in a two-factor enhancement effect (Xu and Ci, 2023). After considering the interactions of the other influencing factors in pairs, the nonlinear enhancement effect was generated.
For tourists with mid-high education levels, the q-values of (X1 Ո X2), (X1 Ո X6), (X1 Ո X8), (X2 Ո X8) and (X8 Ո X9) were greater than the maximum q-value of a factor. This indicates that awareness of carbon reduction (X1) interacted with the cognition of carbon emissions (X2), cognition of low-carbon tourism costs (X6), perception of institutional restriction (X8) and others, to bring about a double-factor effect. Likewise, the cognition of carbon emissions (X2) interacts with the perception of institutional restriction (X8) to produce a double-factor enhancement effect. The perception of institutional restriction (X8) and cognition of external conditions (X9) also interact to produce a double-factor enhancement effect (Xu and Ci, 2023). After considering the interactions of the other impacting factors in pairs, the nonlinear enhancement effect was produced. Among them, the strongest influence is the interaction factor between the awareness of carbon reduction (X1) and the cognition of external conditions (X9), with an interaction value of 0.223, indicating that the awareness of carbon reduction (X1) has a greater impact on the low-carbon tourism cognition of mid-high education level tourists.
For tourists with high education levels, the q-values of (X1 Ո X7), (X1 Ո X9), (X2 Ո X8) and (X8 Ո X9) are greater than maximum q-value of a factor. This suggests that the awareness of carbon reduction factor (X1) interacts with social reference standards (X7) and cognition of external conditions factor (X9) to produce a double-factor enhancement effect. The cognition of carbon emissions (X2) interacted with the perception of the institutional restriction factor (X8), and the perception of institutional restriction factor (X8) interacted with the cognition of the external conditions factor (X9), both of which also produced double-factor enhancement effects. After considering the interactions of other influencing factors in pairs, the nonlinear enhancement was presented. The most influential factor was the interaction between the social reference standards (X7) and the cognition of external conditions (X9), with an interactive influence value reaching 0.505, indicating that the social reference standards (X7) had a stronger effect on the low-carbon tourism cognition of high-level tourists.

5 Discussion and suggestions

5.1 Discussion

Currently, scholars are paying more and more attention to the low-carbon tourism cognition of tourists, and they agree that the level of cognition positively affects tourist behavior (Tanford and Montgomery, 2014). Improving tourists’ cognition level can lead to the further development of low-carbon tourism. The existing literature shows a relatively simple research design for low-carbon tourism cognition. For example, many studies only focus on tourists’ cognition of a single object, such as aviation or tourism shopping, or they examine the overall cognition of the tourism environment and the general background of climate change (Mair, 2011; Seetaram et al., 2018). Meanwhile, the exploration of influencing factors mainly focuses on the internal levels of individuals or only on the external environment (Shewmake et al., 2015). These approaches can only give a rough understanding of tourists’ low-carbon cognition in which the cognition level of tourists with respect to different low-carbon tourism activities cannot be identified, and tourists’ cognitive shortfalls of low-carbon behavior cannot be effectively solved. To fill this research gap, this study incorporated six activities of low-carbon tourism into an analysis of tourists’ cognition level of low-carbon tourism and puts forward the following targeted and valuable suggestions.
Regarding the relationship between education level and low-carbon cognition, scholars have analyzed the overall low-carbon cognition based on tourist demographic characteristics such as age, gender, occupation, income and education. This study found that education level is the main factor impacting tourists’ low-carbon tourism cognition (Horng et al., 2013; Hu et al., 2013; Teng et al., 2014). The higher the education level, the higher the awareness of developing low-carbon tourism, and the more inclined tourists are to support low-carbon tourism (Carr, 2002; Wu et al, 2017; Liu and Suk, 2021). However, previous studies have been too general and lacked pertinence. This study also analyzed the coupling degree of the six cognition aspects of low-carbon tourism activities for tourists with different education levels. The coupling degree model results showed that the higher the education level, the higher the comprehensive development level of low-carbon tourism cognition (Wu et al, 2017). The results also revealed a coupling interaction between the six cognition aspects, in which the comprehensive development level of the cognition of low-carbon transport and the cognition of low-carbon accommodation were relatively higher, while the development of the cognition of low-carbon visiting was relatively lower (Liu and Suk, 2021). The study findings indicate that with an increase in education level, the degree of coupling coordination constantly improved, and the coordination level moved from intermediate to good coordination. The four groups of tourists, based on education level, showed different cognition weaknesses. Specifically, both the low-level and mid-low-level groups were weak in low-carbon visiting cognition, while the mid-high level group was lacking in low-carbon shopping cognition, and the high-level group was weak in low-carbon food cognition. Compared with previous studies, this study explains the relationships between education level and different low-carbon tourism activities. These findings can help to identify the weak points of low-carbon tourism cognition for tourists with different education levels, which will be helpful for targeted low-carbon education.
In determining the factors that impact tourists’ low-carbon cognition, some studies have explored the determining factors including public consciousness, personal characteristics, the strength of government organizations, relevant policy rationality and the low-carbon environment of tourist destinations (Hsieh et al., 2016; Bogicevic et al., 2018; Emberger-Klein and Menrad, 2018; Rasoolimanesh et al., 2020; Zhang and Zhang, 2020). Studies have indicated that the awareness of the public is the primary consideration, the strength of government organizations is the external guarantee factor and the low-carbon environment of tourist destinations has a certain positive impact on tourists’ low-carbon cognition (Rosenbloom, 2017). Throughout the previous studies, there has been no systematic exploration of the factors influencing low-carbon tourism cognition. This study used the Geo-detector method to identify the main factors that impact low-carbon tourism cognition for tourists with different education levels. The results show that the nine impacting factors had significant decisive effects on tourists’ low-carbon tourism cognition, and any combination of two factors showed double-factor or nonlinear enhancement. Specifically, the awareness of carbon reduction was the main driving force for the low, mid-low, and mid-high-level groups, while the social reference standards were the main driving force for the high-level group. These findings provide implications for improving the low-carbon tourism cognition of different tourist groups.

5.2 Suggestions

The findings of this study suggest that low-carbon education can be carried out in a more targeted way. By revealing the relationships between education level and different tourism aspects, the characteristics of tourists with different education levels in low-carbon tourism can be identified. For tourists with low education levels, efforts should be made to seek a joint effect of internal and external factors. While relying on external factors such as institutional standards and social restrictions, the education related to low-carbon emissions should be strengthened, especially improving their cognition level for low-carbon visiting. For tourists with mid-low education levels, the focus should be on improving their subjective awareness of carbon reduction, individual-social responsibility and cognition of low-carbon visiting. For tourists with mid-high education levels, it is critical to improve their cognition of carbon emissions and low-carbon shopping, and to popularize the specific mechanisms of carbon emissions. For tourists with high education levels, the scenic spots should provide more routes for low-carbon tourism, add channels for low-carbon tourism behavior and improve their cognition of low-carbon food in order to promote behavior. In addition, specific measures can be provided for helping tourists to realize low-carbon tourism options. Through an analysis of the influencing factors and their mechanisms, it is possible to determine the important driving factors of low-carbon cognition among tourists with different education levels.
This study is not without limitations, and we propose some improvements for future research. Other factors influencing low-carbon tourism cognition should be further analyzed vertically, such as the degree of correlation between low-carbon tourism cognition and other demographic characteristics like age, occupation and income (Ma et al., 2020). At the same time, the interaction mechanisms between low-carbon cognition and other external environmental factors, such as government policies and social norms, should be explored horizontally. Additionally, in order to provide further verification of the scales, empirical analyses should be carried out in a number of different places.

6 Conclusions

(1) According to the coupling coordination degree, the six cognition aspects of low-carbon tourism gradually transition from intermediate coordination to good coordination with the advancement of education level. Both the low-level and lower-level tourists belong to the lag type of low-carbon visiting cognition, while the higher-level tourists belong to the lag type of low-carbon shopping cognition, and the high-level tourists are the lag type of low-carbon food cognition.
(2) According to the single factor and interactive detections in the geographical detector, each impacting factor has a decisive effect on tourists’ cognition of low-carbon tourism, and the effect of any two factors after interaction shows a double-factor or nonlinear enhancement. The low-level tourists have awareness of carbon reduction as the main driving factor, while the individual-social responsibility is relatively weaker. The lower-level tourists also have a stronger feedback effect on the awareness of carbon reduction, while the feedback effect of man-land values is weaker. The higher-level tourists still have awareness of carbon reduction as the main factor, while individual professional skills play the role of an obstacle. The social reference standards have a greater influence on the high-level tourists’ cognition, while the cost of low-carbon cognition is relatively weaker.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Amelung B, Moreno A. 2012. Costing the impact of climate change on tourism in Europe: Results of the PESETA project. Climatic Change, 112(1): 83-100.

[2]
Amengual A, Homar V, Romero R, et al. 2012. Projections of the climate potential for tourism at local scales: Application to Platja de Palma, Spain. International Journal of Climatology, 32(14): 2095-2107.

[3]
Atzori R, Fyall A, Tasci A D A. 2019. The role of social representations in shaping tourist responses to potential climate change impacts: An analysis of Florida’s coastal destinations. Journal of Travel Research, 58(8): 1373-1388.

[4]
Becken S, Simmons D G, Frampton C. 2003. Energy use associated with different travel choices. Tourism Management, 24(3): 267-277.

[5]
Bhochhibhoya S, Pizzol M, Marinello F, et al. 2019. Sustainability performance of hotel buildings in the Himalayan region. Journal of Cleaner Production, 250(20): 1-10.

[6]
Bogicevic V, Bujisic M, Cobanoglu C, et al. 2018. Gender and age preferences of hotel room design. International Journal of Contemporary Hospitality Management, 30(6): 10-17.

[7]
Burgess J, Harrison C M, Filius P. 2016. Environmental communication and the cultural politics of environmental citizenship. Environment & Planning A, 30(8): 1445-1460.

[8]
Burke T A, Cascio W E, Costa D L, et al. 2017. Rethinking environmental protection:Meeting the challenges of a changing world. Environmental Health Perspectives, 125(3): 43-49.

[9]
Cannon T F, Ford J. 2002. Relationship of demographic and trip characteristics to visitor spending: An analysis of sports travel visitors across time. Tourism Economics, 8(3): 263-271.

[10]
Carr N. 2002. A comparative analysis of the behavior of domestic and international young tourists. Tourism Management, 23(3): 321-325.

[11]
Cheng Z H, Cheng J H, Zhang A J. 2018. Study on tourists’ cognition of low-carbon tourism and the impacting factors in the Wutai Mountain scenic area. Tourism Tribune, 33(3): 50-60. (in Chinese)

[12]
Chou C P, Bentler P M. 2002. Model modification in structural equation modeling by imposing constraints. Computational Stats & Data Analysis, 41(2): 271-287.

[13]
Dwyer C. 2011. The relationship between energy literacy and environmental sustainability. Low Carbon Economy, 2(3): 123-137.

[14]
Eijgelaar E, Nawijn J, Barten C, et al. 2016. Consumer attitudes and preferences on holiday carbon foot-print information in the Netherlands. Journal of Sustainable Tourism, 24(3): 398-411.

[15]
Emberger-Klein A, Menrad K. 2018. The effect of information provision on supermarket consumers’ use of and preferences for carbon labels in Germany. Journal of Cleaner Production, 172: 253-263.

[16]
Esteve-Llorens X, Dias A C, Moreira M T, et al. 2020. Evaluating the Portuguese diet in the pursuit of a lower carbon and healthier consumption pattern. Climatic Change, 162(4): 2397-2409.

[17]
Frumhoff P C, Heede R, Oreskes N. 2015. The climate responsibilities of industrial carbon producers. Climatic Change, 132(2): 157-171.

[18]
Fu J, Zhang Q, Wang P, et al. 2022. Spatio-temporal changes in ecosystem service value and its coordinated development with economy: A case study in Hainan Province, China. Remote Sensing, 14(4): 970-981.

[19]
Galloway G, Mitchell R, Getz D, et al. 2008. Sensation seeking and the prediction of attitudes and behaviors of wine tourists. Tourism Management, 29(5): 950-966.

[20]
Grazzini L, Acuti D, Aiello G. 2020. Solving the puzzle of sustainable fashion consumption: The role of consumers’ implicit attitudes and perceived warmth. Journal of Cleaner Production, 287: 125579. DOI: 10.1016/j.jclepro.2020.125579.

[21]
Guo A N, Zhao Z Q, Yuan Y, et al. 2018. Quantitative correlations between soil and plants in reclaimed mining dumps using a coupling coordination degree model. Royal Society Open Science, 5: 180484. DOI: 10.1098/rsos.180484.

[22]
Hindley A, Font X. 2014. Ethics and influences in tourist perceptions of climate change. Current Issues in Tourism, 20(16): 1684-1700.

[23]
Horng J-S, Hu M-L, Teng C-C, et al. 2013. Development and validation of the low-carbon literacy scale among practitioners in the Taiwanese tourism industry. Tourism Management, 35(4): 255-262.

[24]
Horng J-S, Hu M-L, Teng C-C, et al. 2014. Energy saving and carbon reduction behaviors in tourism: A perception study of Asian visitors from a protection motivation theory perspective. Asia Pacific Journal of Tourism Research, 19(6): 721-735.

[25]
Hsiao T Y. 2016. Developing a dual-perspective low-carbon tourism evaluation index system for travel agencies. Journal of Sustainable Tourism, 24(12): 1604-1623.

[26]
Hsieh C-M, Park S H, Mcnally R. 2016. Application of the extended theory of planned behavior to intention to travel to Japan among Taiwanese youth: Investigating the moderating effect of past visit experience. Journal of Travel & Tourism Marketing, 33(5): 717-729.

[27]
Hu M-L, Horng J-S, Teng C-C, et al. 2013. Assessing students’ low carbon literacy by Ridit IPA approach. Journal of Hospitality Leisure Sport & Tourism Education, 13(1): 202-212.

[28]
Juvan E, Dolnicar S. 2014. The attitude-behavior gap in sustainable tourism. Annals of Tourism Research, 48: 76-95.

[29]
Kim A K. 2012. Determinants of tourist behavior in coastal environmental protection. Tourism Geographies, 14(1): 26-49.

[30]
Koo C, Kim H, Hong T. 2014. Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities. Energy Policy, 73(10): 356-367.

[31]
Leonidou L C, Coudounaris D N, Kvasova O, et al. 2015. Drivers and outcomes of green tourist attitudes and behavior: Sociodemographic moderating effects. Psychology & Marketing, 32(6): 635-650.

[32]
Li D M, Yang Y Y, Du G M, et al. 2021. Understanding the contradiction between rural poverty and rich cultivated land resources: A case study of Heilongjiang Province in Northeast China. Land Use Policy, 108(9): 105673. DOI: 10.1016/j.landusepol.2021.105673.

[33]
Li Y F, Li Y, Zhou Y, et al. 2012. Investigation of a coupling model of coordination between urbanization and the environment. Journal of Environmental Management, 98(1): 127-133.

[34]
Lin Y H, Hemmington N. 1997. The impact of environmental policy on the tourism industry in Taiwan. Progress in Tourism & Hospitality Research, 3(1): 35-45.

[35]
Liu Y M, Suk S. 2021. Constructing an evaluation index system for China’s low-carbon tourism region: An example from the Daxinganling region. Sustainability, 13(21): 12026. DOI: 10.3390/su132112026.

[36]
Lu A C C, Gursoy D, Chiappa G D. 2016. The influence of materialism on ecotourism attitudes and behaviors. Journal of Travel Research, 55(2): 1-14.

[37]
Ma D Q, Hu J S, Yao F J. 2020. Big data empowering low-carbon smart tourism study on low-carbon tourism O2O supply chain considering consumer behaviors and corporate altruistic preferences. Computers & Industrial Engineering, 153(3): 107061. DOI: 10.1016/j.cie.2020.107061.

[38]
Mair J. 2011. Exploring air travelers’ voluntary carbon-offsetting behavior. Journal of Sustainable Tourism, 19(2): 215-230.

[39]
McKercher B, Prideaux B, Cheung C, et al. 2010. Achieving voluntary reductions in the carbon footprint of tourism and climate change. Journal of Sustainable Tourism, 18(3): 297-317.

[40]
Rasoolimanesh S M, Khoo-Lattimore C, Noor S M, et al. 2020. Tourist engagement and loyalty: Gender matters? Current Issues in Tourism, 5(13): 22-34.

[41]
Rosenbloom D. 2017. Pathways: An emerging concept for the theory and governance of low-carbon transitions. Global Environmental Change, 43: 37-50.

[42]
Scott D, Amelung B, Becken S, et al. 2007. Climate change and tourism: Responding to global challenges. Climate Change & Tourism Responding to Global Challenges, 12(4): 168-181.

[43]
Seetaram N, Song H, Ye S, et al. 2018. Estimating willingness to pay air passenger duty. Annals of Tourism Research, 72: 85-97.

[44]
Shewmake S, Okrent A, Thabrew L, et al. 2015. Predicting consumer demand responses to carbon labels. Ecological Economics, 119: 168-180.

[45]
Skamp K, Boyes E, Stanisstreet M. 2013. Beliefs and willingness to act about global warming: Where to focus science pedagogy. Science Education, 97(2): 191-217.

[46]
Tanford S, Montgomery R. 2014. The effects of social influence and cognitive dissonance on travel purchase decisions. Journal of Travel Research, 54(5): 596-610.

[47]
Tang Z. 2015. An integrated approach to evaluating the coupling coordination between tourism and the environment. Tourism Management, 46: 11-19.

[48]
Teng C-C, Horng J-S, Hu M-L, et al. 2014. Exploring the energy and carbon literacy structure for hospitality and tourism practitioners: Evidence from hotel employees in Taiwan. Asia Pacific Journal of Tourism Research, 19(4): 451-468.

[49]
Tervo-Kankare K, Hall C M, Saarinen J. 2013. Christmas tourists’ perceptions to climate change in Rovaniemi, Finland. Tourism Geographies, 15(2): 292-317.

[50]
Wang J F, Xu C D. 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116-134. (in Chinese)

DOI

[51]
Weaver C P, Miller C A. 2019. A framework for climate change-related research to inform environmental protection. Environmental Management, 64(3): 245-257.

DOI PMID

[52]
Wu W J, Zhang X L, Yang Z P, et al. 2017. Creating a low carbon tourism community by public cognition, intention and behaviour change analysis: A case study of a heritage site (Tianshan Tianchi, China). Open Geosciences, 9(1): 197-210.

[53]
Xu Z H, Ci F Y. 2023. Spatial-temporal characteristics and driving factors of coupling coordination between the digital economy and low-carbon development in the Yellow River Basin. Sustainability, 15(3): 2731. DOI: 10.3390/su15032731.

[54]
Zhang J K, Zhang Y. 2020. Examining the energy literacy of tourism peasant households in rural tourism destinations. Asia Pacific Journal of Tourism Research, 25(4): 441-456.

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