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Article

Study on the Strength of Rural Tourism Operators’ Willingness to Carbon Offset and Its Influencing Mechanisms

by
Wei Song
,
Liguo Wang
*,
Yan He
,
Yanting Ye
and
Yuting Jiang
School of Land Resources and Environment, Jiangxi Agricultural University, No.1101 Zhimin Road, Xinjian District, Nanchang 330045, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 6253; https://doi.org/10.3390/su16146253
Submission received: 9 June 2024 / Revised: 9 July 2024 / Accepted: 17 July 2024 / Published: 22 July 2024
(This article belongs to the Section Sustainable Urban and Rural Development)
Browse Figures
Versions Notes

Abstract

:
Tourism operators generate carbon emissions during their operations, and their environmental responsibility behaviors, such as carbon offsetting, significantly impact the ecological environment of tourist sites. Understanding the operators’ willingness to engage in carbon offsetting and the factors influencing this willingness is crucial for achieving the “dual carbon” goals and promoting sustainable growth in China’s tourism sector. This study collected 746 offline questionnaires from operators at rural tourism sites across 100 counties and districts in Jiangxi Province. It empirically analyzed their willingness to participate in carbon offsetting, comparing various factors such as the strength of willingness, spatial differences, and distinctions between all-for-one tourism counties and non-all-for-one tourism counties, as well as suburban, outer suburban, and remote tourism sites. Using the value–belief–norm theory and the theory of planned behavior, a structural equation model was developed to comprehensively examine the operators’ willingness and its influencing mechanisms. The structural statistical analysis revealed that the integrated model effectively predicted rural tourism operators’ willingness to participate in carbon offsetting. The results showed that, except for Hypothesis 6, all suggested correlations between the variables were significant. Value perception had a significantly positive effect on participants’ desire to engage in carbon offsetting. This research provides various theoretical and practical implications for local authorities regarding rural tourism operators, advancing the incentive for carbon offsetting and sustainable tourism development.

1. Introduction

Currently, the severe issue of climate change brought on by emissions of greenhouse gases, mostly carbon dioxide, has attracted a great deal of attention from the international community and academic research on low-carbon emission reduction [1,2,3]. In order to meet the challenge of global climate change, the Chinese government formally proposed at the 75th session of the United Nations General Assembly the goal of achieving carbon peak by 2030 and carbon neutrality by 2060. Subsequently, the Chinese government has successively issued the Action Plan for Carbon Dioxide Peaking Before 2030, the Working Guidance for Carbon Dioxide Peaking and the Carbon Neutrality in Full and Faithful Implementation of the New Development Philosophy, and other documents for building an ecological civilization more quickly and realizing high-quality economic development.
Tourism was known as a “smokeless industry” in the early days due to its low waste emissions and pollution [4], but in recent years, the total carbon emissions from tourism have been on the rise [5]. According to data from the World Tourism Organization, by 2030, carbon emissions from tourism worldwide are predicted to make up 5–14% of all carbon emissions from human activity and will keep expanding at a 2.5% annual rate if measures are not taken to control them [6]. Therefore, low-carbon tourism is one of the important ways to fulfill China’s dual-carbon goals and sustainable development. The notion of a “low-carbon economy” is specifically applied to the tourism industry as “Low-Carbon Tourism” [7]. Rural tourism is a comprehensive industry involving multiple sectors and industries, which not only involves traditional industries such as tourism, agriculture and forestry but is also closely related to commerce, transportation, land, finance and other sectors [8]. Meanwhile, rural tourism is now recognized and embraced as a key strategy for reviving rural economies and societies without jeopardizing sustainable development fundamentals [9,10]. However, environmental problems such as atmospheric pollution and vegetation destruction are becoming more and more prominent in the process of rapid growth in rural tourism [11], which restricts the high-quality development of rural tourism in China. As a fusion of low-carbon economy and rural tourism, rural low-carbon tourism is an important hand in implementing sustainable development and the new development pattern [12]. How to build beautiful villages that are pleasant to live in, visit and work in, and how to encourage the development of low-carbon tourism in villages have received great attention from the academic community.
Carbon offset is a vital link that supports the growth of low-carbon tourism [13], aiming to incentivize actors to reduce carbon emissions or increase carbon credits by raising the cost of carbon emissions or increasing the benefits of carbon credits, in order to fulfill the purpose of protecting the ecological environment and promoting harmony between people and the land [14]. Goessling [15] introduced the concepts of “carbon offset” and “carbon offset destination” in tourism research. Currently, the research field on tourism carbon offsetting mainly involves the following four major aspects, first, measurement of the tourism carbon footprint [16,17]; second, research on the tourism carbon offset policies [18,19]; third, research on the tourism carbon offset projects [20,21]; and fourth, research on the willingness of participation of tourism carbon offset stakeholders [22,23]. Research on tourism carbon offset stakeholders mainly focuses on objects such as tourists, farmers and governments, such as in Hongrun Wu et al.’s research [24], where they used a hybrid choice model to explore tourists’ preferences and willingness to pay for travel carbon offset, and Habesland et al. [25], where they used a logistic regression model to study Norwegian forest owners’ attitudes and eagerness to take part in the carbon offset program. Qiao Peng et al. [26] examined how local governments made decisions both before and after the introduction of reward and punishment systems. However, there are fewer domestic and international studies on tour operators’ participation in carbon offsetting. The behavior of operators, as one of the important carbon emitters in tourism activities, is crucial for achieving sustainable tourism development [27].
Borden and Schettino [28] thought that ecological responsibility is characterized by individual or collective efforts to mitigate harm to the environment. Carbon-offsetting behavior, which reduces its own negative impact on climate change through the use of clean energy or environmental protection projects, also belongs to one kind of environmentally responsible behavior [29]. In recent years, theoretical models such as the theory of planned behavior, the values–beliefs–norms theory and the norm activation theory have been used to study environmentally responsible behavior [30,31,32]. Subjective norms, the willingness of the individual, and the impact of perceived behavioral control on conduct are the main topics of the theory of planned behavior [33]. Nevertheless, it does not fully consider the influence of irrational factors such as individual emotions on behavioral decisions. The value–belief–norm theory, on the other hand, focuses on the influence of individuals’ values, beliefs, and social norms on behavior [34]. It was shown that the integrated model of the value–belief–norm theory and the theory of planned behavior were widely validated for explaining individuals’ environmentally responsible behavior [35,36,37]. Therefore, the integrated model according to the value–belief–norm theory and the theory of planned behavior can better explain the influencing mechanism of carbon offsetting willingness of rural tourism land operators.
However, there is still a gap in the research on the validation of the environmental responsibility behavior of tourism destination operators using the integrated model developed by the value–belief–norm theory and the theory of planned behavior, i.e., there is a pressing need for empirical evidence of the application of this model in tourism scenarios. Therefore, we would like to study the carbon offsetting willingness of tourism destination operators within this theoretical framework and look for potential influencing mechanisms, which is the most important research objective of this study.
On the basis of existing research, this paper focuses on China’s “dual-carbon” goal and rural revitalization strategy, and pro-environmental behaviors and their influencing factors from the perspective of green and low-carbon development in an attempt to clarify the interrelationships between value perception, awareness of consequence and willingness to participate. Using a combination of statistical analysis and model construction, this study explores the strength of operators’ willingness to participate (WTP) in carbon offsetting and its influencing mechanism from the perspective of green and low-carbon development, elucidates the correlation effect between the two, and puts forward optimization suggestions for relevant policy paths and institutional arrangements, so as to provide theoretical support and decision-making reference for the management of human habitat in tourism areas, rural environmental protection and its green and low-carbon development, and the ecological supply of leisure agriculture and rural tourism products provide theoretical support and decision-making reference. Specifically, we visited 746 operators of rural tourism sites in Jiangxi Province and conducted questionnaire surveys with them to obtain sufficient survey data covering all 100 districts and counties in Jiangxi Province. In addition to discussing the factors affecting tour operators’ willingness to carbon offset, we also examined the spatial differences exhibited by operators’ willingness to carbon offset under different conditions, which is another very important objective of our study. The marginal contributions of this paper may be as follows: first, to measure the strength and spatial distribution characteristics of rural tourism operators’ participation in carbon offsetting in Jiangxi Province, and to compare the differences in the willingness of different types of rural tourism operators to participate in carbon offsetting; second, to explore the key roles in the mechanism of operators’ willingness to participate in carbon offsetting in order to improve the motivation of tourism operators to participate in carbon offsetting; and third, to increase the micro-objective under the mechanism of tourism carbon-offsetting behavioral willingness to provide a theoretical basis for the sustainable development of rural tourism.
The rest of the content is organized as follows: Section 2 constructs a theoretical model applicable to operators’ carbon offsetting for tourism, and based on this, ten research hypotheses are proposed. Section 3 describes the basic methods of questionnaire design and questionnaire data recovery. Section 4 presents the results of the study, including the analysis of spatial differences in operators’ willingness to compensate for carbon, the analysis of the mechanism influencing operators’ willingness to compensate for carbon, and the test of the mediating effect. Section 5 and Section 6 present the discussion and conclusion, respectively.

2. Theoretical Basis and Hypotheses Development

2.1. Value–Belief–Norm (VBN) Theory

The Value–Belief–Norm (VBN) theory is a behavioral analysis framework, proposed by scholar Stern founded on the new environmental paradigm theory, value theory, and norm activation theory [34]. This theory is an expansion of the Norm Activation Model (NAM) of altruism [38], which holds that a person acts altruistically whether or not they are aware that other(s) are in danger or are threatened; this should highlight the consequences of failing to address the other(s)’ problem and they assign themselves responsibility for the helping actions. Regardless of the occurrence of both psychological circumstances, helping behavior is triggered by sentiments of moral obligation—that is, the moral or personal norm—to assist the other or others [39]. These concepts illuminate the moral normative basis of environmentalism, or more precisely, the inclination to act in a way that is environmentally friendly [40]. This theory concentrates on how values such as altruism, egoism, and traditionalism influence beliefs such as the new ecological paradigm, which in turn impacts individual norms and behaviors [40].
In recent years, many scholars have used it to study individuals’ environmental-friendly actions [37,41] and the research suggests that individuals’ values act on pro-environmental behaviors through the awareness of consequences, ascription of responsibilities and personal norms [42]. Within the VBN causal chain, personal norm (PN) is activated by the ecological worldview, which is determined by individual value. The ecological worldview then directly affects the perception of consequences, which in turn prompts the attribution of responsibility [43]. Value perception (VP) is usually associated with three value orientations, i.e., biosphere-friendly values, altruistic values, and egoistic values [44]. Tour operators’ VP of carbon-offsetting behavior are subjective feelings of value to themselves, others, and to the ecosystem. The stronger the perceived value of participating in carbon-offsetting behaviors, the stronger the pro-environmental beliefs of tourism operators, which in turn stimulate their own ethical constraints and thus initiate carbon-offsetting behaviors [31]. Awareness of consequences (AC) usually refers to an individual’s recognition and assumption of the adverse effects that may result from his or her failure to adopt a particular pro-social behavior [45]. Ascription of responsibility (AR) describes the sense of responsibility one has when one fails to engage in a particular pro-environmental activity which has an adverse effect [46]. Academic research has amply demonstrated that AC and AR activate PN directly, either AC activates PN indirectly through AR and that VP can activate PN directly through AC and AR [47]. PN is a person’s value-based self-expectation that reflects their sense of obligation or responsibility to implement environmentalism [48]. When tour operators perceive that participation in carbon-offsetting behavior will have a positive effect on themselves and the environment, the stronger their AC is, thus stimulating their sense of responsibility and the more they will regulate their own behavior [13,49]. Summarizing the above analysis, the following hypotheses are put forth in this paper:
Hypothesis 1 (H1).
VP has a significantly positive effect on the AC in tour operators’ carbon-offsetting behavior.
Hypothesis 2 (H2).
AC has a significantly positive effect on the AR in tour operators’ carbon-offsetting behavior.
Hypothesis 3 (H3).
AR has a significantly positive effect on the PN in tour operators’ carbon-offsetting behavior.
Hypothesis 4 (H4).
PN has a significantly positive effect on the WTP in tour operators’ carbon-offsetting behavior.

2.2. Theory of Planned Behavior (TPB)

Theory of Planned Behavior (TPB) is Ajzen’s expansion founded on the theory of rational behavior [50]. According to the concept, behavioral intention is the immediate precondition for an individual’s conduct and is impacted by three distinct factors: attitudes toward the behavior, perceived behavioral control, and subjective norms [51]. The formation of individual behavioral attitude (BA) is based on opinions regarding the perceived advantages and costs of a particular behavior [52]. BA towards carbon offset implementation by tourism operators are the operators’ positive or negative evaluations of the implementation of carbon offset behavior. Generally speaking, an individual’s BA is consistent with the direction of behavioral intention. In terms of perceived behavioral control (PBC), tour operators’ perception of the difficulty of participating in carbon-offsetting behavior, which is a subjective reflection of their control over their own controllable factors and external stimuli [53]. Tourism operators’ participation in carbon offsetting may be affected by their own factors such as income, literacy and ability to adopt carbon offsetting measures, as well as by external factors such as the local economy, institutions and publicity. Subjective norm (SN) refers to the social or public opinion pressure felt by tourism operators when making a behavioral decision [54], which may come from groups such as friends and family, mass media, etc., and can lead to the formation of good ecological values and discipline tourism operators to implement carbon-offsetting behaviors. Several studies were carried out to demonstrate the positive relationship between BA, PBC and SN in the context of pro-environmental behaviors and to emphasize TPB’s predictive validity and how well it explains intentions for environmentally friendly behavior [30,35]. For example, Mancha and Yoder [55] identified attitudes, social norms and PBC as factors that influence a person’s propensity to adopt eco-friendly practices in order to eventually reduce their carbon footprint. Lin Yi et al. [56] found that SN, BA and PBC has a significantly positive effect on the willingness of rural tourism operators to participate. Therefore, the following hypotheses are put forth in this paper:
Hypothesis 5 (H5).
BA has a significantly positive effect on the WTP in tour operators’ carbon-offsetting behavior.
Hypothesis 6 (H6).
PBC has a significantly positive effect on the WTP in tour operators’ carbon-offsetting behavior.
Hypothesis 7 (H7).
SN has a significantly positive effect on the WTP in tour operators’ carbon-offsetting behavior.

2.3. Integration of the Value–Belief–Norm Theory and the Theory of Planned Behavior

Although the value–belief–norm theory and the theory of planned behavior are widely used in various scenarios, both theories have their own limitations [57]. The values–belief–norm theory emphasizes individual values and moral constraints and disregards the impact of reasonable influences on personal behaviors [34]. The theory of planned behavior focuses on predicting individual behavior through self-controllable and external stimuli, ignoring the consideration of the impact of irrational factors on individual behavior [55]. In view of this, domestic and international scholars have avoided the shortcomings of a single theory itself by combining the theory of planned behavior with the value–belief–norm theory [35,36]. The variables of the TBP and VBN theories can be effectively incorporated into robust models to increase the prediction validity of environmental behaviors and intents, according to pertinent studies [58]. Studies have revealed that a person’s VP is the nexus of the dialogue between the above two theories, which influences both AC and AR in the value–belief–norm theory, and then influences PN based on the variables of the two dimensions, which ultimately mediate PN on an individual’s pro-environmental behavior [59]. Moreover, the VBN theory incorporates PN into the model as a predictor of intentions, with SN directly influencing PN, which originates from the original NAM [38]. Before participating in carbon-offsetting behavior, tour operators will evaluate and judge the carbon-offsetting behavior based on their own cognition and external information, which will form the BA of tour operators toward participating in carbon-offsetting behavior. Moreover, tour operators will be influenced by the social environment and group norms to form their own SN, and then adjust their PN [60]. In addition, the individual’s choice is based on the principle of maximizing benefits to mobilize their own resources, and the stronger their VP, the stronger the perception of controllability of participation behavior, i.e., the tour operator’s VP promotes the holding of positive PBC. To summarize the above analysis, the following hypotheses are put forth in this paper:
Hypothesis 8 (H8).
VP has a significantly positive effect on the BA in tour operators’ carbon-offsetting behavior.
Hypothesis 9 (H9).
VP has a significantly positive effect on the PBC in tour operators’ carbon-offsetting behavior.
Hypothesis 10 (H10).
SN has a significantly positive effect on the PN in tour operators’ carbon-offsetting behavior.
To verify the above hypotheses, this paper constructs a structural equation model of the factors affecting operators’ WTP in carbon offsetting based on the theory of value–belief–norm (VBN) and the theory of planned behavior (TPB), and the structure of the model is shown in Figure 1.

3. Methods

3.1. Questionnaire Design and Study Area

In this study, a structured questionnaire was used as a survey tool, with reference to the previous research scale of this research group on the carbon offset decision-making behavior of forest park tourism stakeholders [14], and optimized design of the questionnaire in combination with the actual regional research. The formal questionnaire was distributed on the basis of a small-scale pre-survey and then adjusting the questionnaire items. The questionnaire comprises two main parts, the first part is the basic demographics, for instance, differences in demographic variables such as sex, age, educational level, annual income from tourism operations and tourism business sector. Therefore, the sample collection of demographic characteristics is as shown in Table 1.
Drawing from relevant research on individual willingness to carbon offset [14,25,61,62], the second part is the measurement scale of the variables involved in the study and the third part is the survey of operators’ WTP in carbon-offsetting behavior. This research examined the influencing factors of VP, AC, AR, PN, BA, SN, PBC, WTP. All the scales involved in this paper were measured on a five-point Likert 5 scale [63], with 1 to 5 representing the five answers “totally disagree”, “disagree”, “generally” “agree” and “totally agree”. The specific scales and descriptive statistics are shown in Table 2.
In this study, we selected all 100 districts and counties in Jiangxi Province as the study area. Jiangxi Province is located in the middle and lower reaches of the Yangtze River and belongs to the East China region, which is the most prosperous region in China in terms of economic level and market consumption, and the tourism industry shows a very positive state in the East China region. At the same time, from the point of view of natural conditions, Jiangxi Province is characterized by hilly and mountainous terrain and is rich in forest resources, which is a distinctive feature that distinguishes Jiangxi from other regions in East China (e.g., Jiangsu, Shanghai, Anhui, etc.). The rich forest resources provide sufficient natural conditions for recommending carbon offset policies and pilots, a unique advantage that is not available in other provinces in East China except Fujian. In addition to this, Jiangxi Province is rich in tourism resources, and through decades of development, it has formed a branding system focusing on the advantages of red culture, green culture and ancient culture, combined with the characteristics of local resources. Therefore, exploring the willingness of rural tourism operators in Jiangxi Province to participate in carbon offsetting is not only the basic requirement of green, low-carbon and sustainable development of rural tourism under the goal of “dual-carbon” in China but also a good practice reference and demonstration significance for high-quality development of tourism in the new period. It also provides new ideas for the high-quality development of the tourism industry in the new period, which is of good practical reference and demonstration significance. In addition, where the objective conditions allow, we took into account the different tourism resources in each county and district, as well as the specificity of each destination and other factors, and comprehensively selected the rural tourism research sites in each county and district, and through a combination of interviews and questionnaires, we investigated the willingness of the operators of these rural tourism sites to carry out tourism carbon offsets. The reason for limiting the study area to Jiangxi Province is that, in general, there is a relatively high degree of consistency in the policy documents implemented at the provincial level, while there may be some differences in policy implementation as well as resource allocation between different provinces. In order to prevent these potential differences, we conducted survey visits only for Jiangxi Province.

3.2. Sampling and Data Collection

Prior to the official distribution of the questionnaire, the research conducted two pre-surveys in October and November 2022, with the pre-survey area being the districts and counties near Nanchang, Jiangxi Province, China to fill out the questionnaire in order to assess the comprehensibility and validity of the questionnaire and to make the appropriate changes and improvements based on their feedback. The formal research started in January 2023 and lasted for 6 months, in which the distribution and collection of the questionnaires were closed in June 2023, and the data cleaning and organizing took 1 month. The questionnaire was modified through four seminars. In addition to the research team, the Rural Tourism Development Research Center of Jiangxi Agricultural University recruited 79 interviewers from various colleges, mainly undergraduate and master’s degree and doctoral students, and before the formal research, the research center conducted four technical trainings for all the interviewers to ensure the scientific nature of the interviews and surveys and signed a research integrity commitment to guarantee the authenticity of the data. The research asked the interviewers to clarify the basic concepts of carbon emissions, carbon sinks and carbon offsets to the operators before conducting the survey, and to answer the non-leading conceptual questions raised by the operators during the survey.
The research team visited 100 counties and districts, distributed questionnaires offline through face-to-face interviews, and randomly selected respondents, the research site for the rural tourist attractions in their counties and districts. The research team identified invalid questionnaires based on the following: (1) missing data; (2) inconsistencies or obvious errors in the questionnaire; (3) checking the same box for the whole scale questionnaire, for example, checking “Agree” for all of them; (4) not filling out the questionnaire in accordance with the questions indicated in the questionnaire; (5) incorrect answers by respondents who did not understand the questionnaire contents; and (6) questionnaires completed by others who do not meet the requirements. In the survey, the research center distributed 800 questionnaires, and after the collection and cleaning, there were 746 valid questionnaires with a validity rate of 93.25%.

3.3. Sample Description

There were 746 valid samples in total in this questionnaire study. Of the valid questionnaire data, a total of 380 people, or 50.9%, were male, and the ratio of men to women was basically equal. The age of the sample was concentrated between 36 and 45 years old, accounting for 40.8%. In terms of education level, junior high school and high school education accounted for the majority, accounting for 33.6% and 33.4%, respectively. Respondents’ annual income from tourism business was concentrated in the range of 100,000–200,000 RMB, accounting for 33.4%. The tourism operator industry is divided into six major industries: catering, transportation, shopping, sightseeing, accommodation and entertainment, mainly catering industry, accounting for 49.2%. The descriptive characteristics of the operator sample are shown in Table 3.

4. Results

4.1. Analysis of Operators’ Willingness to Participate in Carbon Offsetting

4.1.1. Strength and Spatial Analysis of Operators’ Willingness to Participate in Carbon Offsetting

Firstly, the observed variables influencing operators’ WTP in carbon offsetting were assigned values on a five-point Likert scale; the assignment scores were 1, 2, 3, 4, and 5 points, respectively. This paper uses SPSS 26.0 to take the average value of the participation willingness of the operators of rural tourism land in 100 counties in the questionnaire, and the average value is 3.7391, which shows that the operators’ participation willingness is not very high. After that, based on the WTP in 100 counties, the operators’ WTP was divided into five levels of higher, high, medium, low and lower using the natural break point method in ArcGIS 10.7 software, as shown in Figure 2.
The operators’ WTP in carbon offsetting in rural tourism in 100 counties and districts in Jiangxi Province shows the characteristics of “small concentration and large dispersion”, in which the operators’ WTP in carbon offsetting in rural tourism in the central and eastern parts of the province is higher and more concentrated, and the spatial distribution of the operators’ WTP in carbon offsetting in other counties does not have any significant characteristics.

4.1.2. Analysis of the Willingness of Operators to Participate in Carbon Offsetting in All-for-One Tourism Counties and Non-All-for-One Tourism Counties

As of 2023, there are seven national demonstration zones for regional tourism in Jiangxi Province, namely Jinggangshan City, Ji’an City; Wuyuan County, Shangrao City; Zixi County, Fuzhou City; Shicheng County, Ganzhou City; Jing’an County, Yichun City; Wuning County, Jiujiang City; and Changjiang District, Jingdezhen City; respectively, and seven provincial-level demonstration zones for regional tourism, namely Nanchang County, Nanchang City; Lushan County, Jiujiang City; Yudu County, Ganzhou City; Pengze County, Jiujiang City; Yiyang County, Shangrao City; Ji’zhou, DistrictJi’an City and Lichuan County, Fuzhou City.
By comparing the mean value of operators’ WTP in carbon offsetting in 14 all-for-one tourism counties and 86 non-all-for-one tourism counties, it can be concluded that the mean value of operators’ WTP in carbon offsetting in all-for-one tourism counties (3.8621) is larger than that of operators’ WTP in carbon offsetting in non-all-for-one tourism counties (3.7191).

4.1.3. Comparison of Willingness to Participate in Carbon Offsetting among Operators of Suburban, Outer Suburb and Remote Tourism Sites

The degree of remoteness determines the location conditions and transportation accessibility of rural tourism sites, which in turn affects the popularity of scenic spots, infrastructure construction and other development conditions. In this paper, rural tourism sites within 5 km of urban areas (provincial cities and county cities) are classified as suburban; those within 5–10 km of urban areas are classified as outer suburb; and those more than 10 km away from urban areas are classified as remote. In this paper, the willingness of rural tourism operators to participate in carbon offsetting is compared and analyzed by using the independent samples T-test method for the suburban, outer suburb and remote types, respectively, and the details are shown in Table 4.

4.2. Study on the Mechanism Influencing Operators’ Willingness to Participate in Carbon Offsetting

4.2.1. Analysis Methods

The statistical software programs AMOS 26.0 and SPSS 26.0 were used to evaluate the data. The measurement model was evaluated in the first part of the two-step data analyses, and the hypotheses were tested by fitting the structural model [64]. Initially, a measurement theory based on the general model fit, construct validity, and construct reliability was tested using confirmatory factor analysis (CFA). Next, the model fit indices and structural equation modeling (SEM) were carried out. Finally, the paper performs model correction and mediation effect tests [65].

4.2.2. Reliability and Validity Analysis

To ensure that the scale sample in the questionnaire is valid, reliable, and the topic design is appropriate, it is required to test the validity and reliability of the sample. In this paper, we test the reliability and validity of the questionnaire (see Table 5).
The Cronbach’s α coefficient of the scale is often computed to assess the internal consistency of the scale. For internal consistency, the values of Cronbach’s α coefficient of each construct ranged from 0.708 to 0.893, the overall content’s Cronbach’s α coefficient of the scale is 0.94, and the Cronbach’s α coefficient of each dimension is greater than 0.7, which indicates that the survey data used in this paper has better reliability and the scale has strong internal consistency [66]. The multi-item scales were assessed by measuring the composite reliability (CR) [67]. The composite reliability ranged from 0.717 to 0.895, exceeding the minimum requirement of 0.60. The range of the standard factor loadings was 0.581 to 0.928, exceeding the established items’ limit value of 0.50 [64]. Additionally, the range of all average variance extracted (AVE) values was 0.463 to 0.739, except for the attribution of responsibility (0.463), which was on the lower side but also greater than the acceptable threshold of 0.36 [64].
The validity test measures the energy efficiency of each question item. This paper examines the validity of the questionnaire data by observing the KMO value and Bartlett’s spherical test [66]. The overall KMO value of the questionnaire is 0.934, which is greater than 0.7, and Bartlett’s spherical test is 0.000, which is less than 0.05, both values all being in line with the standard (Table 6). It indicates that the individual observed variables of latent variables have high correlation and are reasonable for factor analysis. As a result, the validity and reliability tests of this research scale were acceptable.
To make the created questionnaire more thorough, a multifaceted discussion was integrated with previously published relevant research in this paper, each dimension was then further refined. Although some index values are not optimal, they are all within the acceptable range. As a result, the information gathered with this questionnaire is efficient, trustworthy, and amenable to further analysis and practical use.

4.2.3. Goodness-of-Fit Model and Model Modification

Based on the framework of the value–belief–norm theory and theory of planned behavior, this paper constructed a structural equation model (Figure 3). The whole hypothetical model was tested using maximum likelihood estimation (ML), and the results showed that the overall predictive model did not conform to the multivariate norm, and the χ2/df exceeded the critical value, and the p-value of the model was significant. Therefore, the Bollen–Stine bootstrap (n = 2000) method was used for correction [68], and the result after 2000 times of Bollen–Stine bootstrap showed p-value = 0.000, indicating that it differed from the χ2 estimated by the ML method, and the probability of the next worse model with Bollen–Stine was close to 0, indicating that the reason for the significant p-value of the model estimated by the ML method was the sample size was too large rather than the model estimation problem, and the model fitness indexes before and after correction were in the standard range [69]. The goodness-of-fit indexes before and after correction are shown in Table 7.

4.2.4. Path Analysis and Hypothesis Testing

In order to verify the theoretical model of the influence mechanism of tour operators’ WTP in carbon offsetting, this paper constructed a structural equation model. The integrated model based on the value–belief–norm theory and the theory of planned behavior is used to question the influence mechanism of tour operators’ WTP in carbon offsetting, and the results of the hypothesis testing are shown in Table 8.
  • Analysis of influencing factors based on the value–belief–norm theory. In the analysis of Hypothesis 1, the VP of tour operators’ participation in carbon-offsetting behavior has a significant and positive effect on AC (β = 0.656, p < 0.001). In the analysis of Hypothesis 2, AC has a significant and positive effect on AR (β = 0.716, p < 0.001). In the analysis of Hypothesis 3, AR has a significant and positive effect on PN (β = 0.743, p < 0.001). In the analysis of Hypothesis 4, PN has a significant and positive effect on WTP. Therefore, Hypotheses H1, H2, H3, and H4 are valid.
  • Analysis of influencing factors based on the theory of planned behavior. In the analysis of Hypothesis 5, the BA of tour operators’ participation in carbon-offsetting behavior has a significant and positive effect on WTP (β = 0.640, p < 0.001). In the analysis of Hypothesis 7, SN has a significant and positive effect on WTP (β = 0.142, p < 0.001). However, in the analysis of Hypothesis 6, PBC presents a positive but insignificant effect on WTP (β = 0.052, p > 0.01), with the effect of BA on WTP in carbon offsetting being the largest (0.640 > 0.142). Therefore, Hypotheses H5 and H7 are valid, but H6 is not valid.
  • In the integrated model, the value–belief–norm theory and the theory of planned behavior interacted with each other. In the analysis of Hypothesis 8, the VP of tour operators’ WTP in carbon offsetting has a significant positive effect on BA (β = 0.742, p < 0.001). In the analysis of Hypothesis 9, VP has a significant positive effect on PBC (β = 0.434, p < 0.001). In the analysis of Hypothesis 10, SN has a significant positive effect on PN (β = 0.328, p < 0.001). Therefore, Hypotheses H8, H9, and H10 are valid.
Table 8. Path analysis of structural equations.
Table 8. Path analysis of structural equations.
HypothesisUnstandardized EstimationStandardized
Estimation 		Status
Unstd.S.E.t-ValueStd.
H10.6400.05312.0750.656 ***Accepted
H20.5730.04313.3260.716 ***Accepted
H30.6770.05911.4750.743 ***Accepted
H40.4070.0805.0880.292 ***Accepted
H50.6540.0729.0830.640 ***Accepted
H60.0530.0411.2930.052Rejected
H70.1220.0373.2970.142 ***Accepted
H80.7180.04515.9560.742 ***Accepted
H90.4150.0439.6510.434 ***Accepted
H100.2020.0267.7690.328 ***Accepted
Note: *** represents a significance level of 1%.

4.2.5. Mediation Effects Test

This paper attempts to conduct multiple mediation effect analyses by using AC, AR, PN, BA and PBC as mediating variables in the constructed model. The Bootstrap estimation method (n = 2000) recommended by Mackinnon [68] and others was used to test the mediating effect at a 95% confidence interval, and the existence of a mediating effect was judged by observing whether or not the confidence interval contained zero.
From Table 9, it can be seen that:
  • Tour operators’ VP has a significant indirect effect on AR and WTP, with the specific paths being VP → AC →AR (β = 0.470, p < 0.001) and VP → BA → WTP (β = 0.474, p < 0.001), respectively. Moreover, the mediation effect point estimate of VP affecting operators’ WTP in carbon offsetting through PBC was 0.022, and the confidence interval contained 0 in both the bias-corrected and percentile method confidence intervals, which means that this mediation effect was not significant.
  • Tour operators’ AC has a significant indirect effect on PN, the specific path is AC → AR → PN (β = 0.532, p < 0.001).
  • AR or SN can produce significant indirect effects on tour operators’ WTP through PN, respectively, the specific path is ARor SN → PN → WTP (β = 0.037, β = 0.022, p = 0.001).
Table 9. Results of indirect effects with Bootstrap method.
Table 9. Results of indirect effects with Bootstrap method.
Hypothetical PathPoint
Estimate		Bias-Corrected 95%
Confidence Interval		Percentile
95% Confidence Interval		p
Value
LowerUpperLowerUpper
VP → AC → AR0.4700.3850.5680.3840.5670.001
VP → BA → WTP0.4740.3810.5590.3800.5590.001
VP → PBC → WTP0.022−0.0110.054−0.0100.0560.183
AC →AR → PN0.5320.4610.5970.4640.6010.001
AR → PN → WTP0.5510.4760.6250.4760.6250.001
SN → PN → WTP0.0960.0590.1450.0570.1420.001

5. Discussion

In the literature on sustainable tourism, there are frequent attempts to understand and predict the environmentally friendly behavior of operators [11,25,60]. This is because tour operators are significant and helpful stakeholders who may add greatly sustainable contributions to any environmental development. Based on the above results, it is shown that it is necessary to study the carbon-offsetting behavior of rural tourism land operators. Although previous research has combined the VBN theory and TPB theory to study tourists’ carbon-offsetting behavior [21,23,61], few of these focused on operators’ carbon-offsetting behavior, in the context of rural tourism lands.

5.1. Analyzing the Strength and Spatial Variation of Operators’ Willingness to Participate in Carbon Offsetting

From the survey results, the average value of operators’ WTP in carbon offsetting is 3.7391, which is at a moderately high level, and it can be seen that the operators’ WTP in carbon offsetting needs to be further improved. In terms of spatial distribution, the operators’ WTP in carbon offsetting in the 100 rural tourism sites is characterized by “small concentration and large dispersion”. In the comparison of operators’ WTP in carbon offsetting between the all-for-one tourism counties and non-all-for-one tourism counties, the all-for-one tourism counties (3.8621) are larger than the non-all-for-one tourism counties (3.7191), which may be attributed to the fact that the all-for-one tourism counties have a more modernized governance system and governance capacity, and, therefore, the operators’ WTP in carbon offsetting is higher. In terms of the willingness of operators to participate in carbon offsetting in different types of rural tourism sites, the outer suburb type (3.9913) > remote type (3.7368) > suburban type (3.6551), and the difference passed the significance test. Most of the outer suburb-types of rural tourism sites rely on forests and other natural attractions, and the operators focus more on environmental quality. In addition, the distance from the city is closer, the degree of openness to the outside world is higher, the information exchange is smooth, and the infrastructure construction is more complete; therefore, the operators of the outer suburb-type of rural tourism sites have the highest WTP in the carbon offsetting. Remote rural tourism sites may be affected by factors such as distance and infrastructure, and the willingness of operators to participate in carbon offsetting is the second highest. Suburban rural tourism sites may have lower environmental requirements and lower perceived value of carbon offsetting; therefore, their operators’ WTP in carbon offsetting is the lowest.

5.2. Analysis of the Mechanisms Influencing Operators’ Willingness to Participate in Carbon Offsetting

First, this paper explored the effect of VP, AC, AR, PN, PBC, BA and SN on rural tourism land operators. A significant positive, direct and indirect association between these factors was found in the results. Operators with higher VP of carbon offsetting were more concerned about the carbon-offsetting behavior, which affected their AC, PBC and BA. Therefore, Hypotheses 1, 8 and 9 are accepted. These findings supported earlier research that suggested people who have a higher VP seem to care more about pro-environmental behavior [31,43,57].
Secondly, building on earlier research, this study offered an integrated framework based on the value–belief–norm (VBN) theory [34] and the theory of planned behavior (TPB) [33] to explore operators’ carbon offset participating willingness towards rural tourism land. The factors for each concept were identified using the relevant literature review and professional input. The results revealed that with the value–belief–norm framework, AC has a significant positive effect on the AR, AR has a significant and positive effect on the PN, and PN has a significant and positive effect on the WTP, which is comparable to previous studies [36,37] while serving distinct objectives. Thus, Hypotheses 2, 3, and 4 are accepted. Meanwhile, in the theory of planned behavior framework, the BA and SN has a significant, positive effect on the WTP. Moreover, the SN has a significant and positive effect on PN. However, the PBC has no significant effect on the WTP. Thus, Hypotheses 5, 7 and 10 are accepted, but Hypothesis 6 is rejected. This suggests that even if operators can perceive the ability to participate in carbon offsetting, this still does not support their WTP. The participation of operators in carbon offsetting is a complex and integrated issue that requires other factors to work together to support the willingness of operators to participate. These results are similar to previous findings [30,52,67].
Finally, we found that there is a significant mediating role of operators’ AC in the effect of VP on AR, and that AC can influence PN through AR. In addition, operators’ AR and SN have an indirect effect on WTP in carbon offsetting through PN. These results are like previous findings [13,35,36], the present research expands on previous studies. When tour operators have a high level of awareness of the environmental value of the destination, they may become aware of the adverse consequences of environmental degradation on their lives and livelihoods, and this awareness of the consequences enhances the operator’s concern about the environmental problem and indirectly increases the sense of responsibility attributed to the operator’s carbon-offsetting behaviors. As the sense of responsibility increases, operators develop or reinforce relevant personal norms, a process that illustrates the shift from an individual’s awareness of a problem to an intrinsic motivation to act.

6. Conclusions

6.1. Empirical Results

In relation to this viewpoint, this paper investigated tour operators’ WTP in carbon offsetting based on the VBN theory and TPB theory. Specifically, this paper discusses the differences in the willingness to participate in carbon offsetting among different types of rural tourism land operators and the mechanism influencing the willingness of rural tourism land operators to participate in carbon offsetting. Hence, a quantitative offline survey covering 100 counties and districts in Jiangxi Province was carried out. The respondents were a sample of rural tourism land operators (n = 746). The research results of this paper show that: first, the strength of the sample operators’ WTP in carbon offsetting (3.7391) is in the middle to high range, and its spatial distribution presents the characteristics of “small concentration and large dispersion”, and the strength of the operators’ WTP in carbon offsetting is slightly larger in the all-for-one tourism counties (3.8621) than the non-all-for-one tourism counties (3.7191). In terms of operators’ WTP in carbon offsetting in different types of rural tourism sites, the outer suburb type (3.9913) > remote type (3.7368) > suburban type (3.6551), and the difference passes the significance test. Second, operators’ PN, SN, and BA has a direct, positive, and significant influence on the WTP in carbon offsetting, but the degree of influence varies. Third, operators’ VP is an important factor influencing their WTP in carbon offsetting, which can have an impact on WTP through two paths.

6.2. Research Contribution and Implications

6.2.1. Theoretical Contribution and Implications

The contributions of this paper are the following: First, the structural equation model is used to integrate and expand the VBN theory and the TPB theory to explore the comprehensive influence mechanism of operators’ WTP in carbon offsetting, which makes the explanation stronger and the analysis more comprehensive. At present, there are fewer comprehensive studies on operators’ pro-environmental behaviors, and this paper will make up for this research gap; secondly, the object of this study was to cover rural tourism sites in 100 counties and districts in Jiangxi Province, explore the characteristics of the strength of operators’ WTP in carbon offsetting in terms of spatial distribution and compare and analyze the differences in operators’ WTP in carbon offsetting in all-for-one tourism and non-all-for-one tourism counties, and in outer suburban, suburban, and remote rural tourism sites, and analyze the differences in the WTP in carbon offsetting. The comparative study of operators’ WTP in carbon offsetting in different types of rural tourism sites can provide a basis and reference for the design of regional carbon offsetting, which is innovative and of practical significance.
The theoretical implications of this research offer helpful insights into the applicability and universality of VBN theory and TPB theory regarding rural tourism land operators’ WTP in carbon offsetting. This indicates the meaningful integration of the VBN theory and the TPB theory. Hence, the study of these theories is conducive to promoting the willingness of operators to offset carbon towards different destinations. These responses likely came about because of increased pro-environmental behavior, as previous studies have shown [31,43]. The empirical results of this study provide theoretical support for both the TPB theory and the VBN theory. The advantages of integrating the two models are comprehensiveness and refinement. Additionally, it is frequently utilized for applications of theory and modeling in situations involving environmentally responsible behavior. Furthermore, there are a number of environmental protection circumstances connected to sustainable green behavior in which this theoretical construct might be used.

6.2.2. Practical Contribution and Implications

The results of this paper will help to develop strategies for increasing the willingness of rural tourism operators to participate in carbon offsetting, and thus develop sustainable environmental actions for the development of other tourism destinations. Consequently, it is reasonable to introduce the following practical implications:
  • Strengthen publicity and education on the idea of ecological civilization for operators of rural tourism sites, enhance their perception of ecological and environmental value perceptions, and promote the formation of environmental ethics. Meanwhile, attention should be paid to enhancing the AR for carbon offsetting actions by operators to prompt them to realize that they are the key players in environmental protection. By organizing training courses, seminars and other activities, the government should raise the level of operators’ awareness of carbon-offsetting behavior, help them understand the importance and significance of carbon offsetting, as well as the positive impact of participating in carbon-offsetting actions for sustainable development of rural tourism to establish the correct awareness of environmental protection and behavioral habits.
  • Guide rural tourism operators at the institutional level to enhance their awareness of the consequences of carbon offsetting and AR. Strengthen supervision and management and formulate relevant laws and regulations to supervise the carbon emissions of tourism operators. Tourism operators who violate the relevant provisions of carbon compensation should bear the corresponding responsibility, increase the cost of violation by tourism operators, and raise the lower limit of carbon emissions and carbon compensation behavior so that tourism operators are aware of the consequences of non-participation in carbon compensation behavior or high emissions. At the same time, strengthen the supervision of law enforcement departments and public opinion, use the strict enforcement process to make tourism operators implement the spirit of laws and regulations, and through the correct guidance of public opinion, monitor and regulate the carbon compensation behavior of tourism operators, so as to achieve the purpose of constraints.
  • The enhancement of the WTP in carbon offsetting is a systematic project [70], which should enhance the willingness of rural tourism operators to participate in carbon offsetting from multi-dimensional factors such as SN, BA and PBC. By setting up a number of typical operators who actively participate in carbon offsetting, playing the role of demonstration and leading, publicizing and promoting the advanced experience and achievements of the typical operators, and guiding other operators to actively participate in carbon offsetting actions. Formulate relevant incentive policies, and provide incentives such as rewards, subsidies and tax exemptions to rural tourism operators who actively participate in carbon offsetting to reduce the resistance of operators to participate in carbon offsetting.

7. Limitations and Future Research

This research has certain limits, even though it makes several significant theoretical and practical contributions. Firstly, this research empirically analyzed only one study area, rural tourism sites in 100 counties and districts in Jiangxi Province, China, which may not be representative of other types of destinations. The relationship between operators’ WTP in carbon offsetting and their VP may be influenced to varying degrees by different types of tourism destinations; therefore, future research could attempt to concentrate on other types of tourism destinations. As a result, attention must be taken when applying the study’s conclusions to different situations.
Secondly, the empirical analysis through structural equation modeling ignores the influence of individual or other factors that may affect operators’ WTP in carbon offsetting. Furthermore, a thorough examination that considers various age groups, generations and educational levels is required for subsequent research.
Finally, in exploring the characteristics of spatial distribution, there are no significant characteristics of operators’ WTP in carbon offsetting, which may be a defect of insufficient sample size. Future research can further expand the sample size to more comprehensively analyze the spatial distribution characteristics of operators’ WTP in carbon offsetting.

Author Contributions

Conceptualization, W.S. and L.W.; methodology, W.S.; software, W.S.; validation, W.S., Y.H. and Y.J.; formal analysis, W.S.; investigation, W.S., L.W., Y.H., Y.Y. and Y.J.; resources, L.W.; data curation, W.S.; writing—original draft preparation, W.S.; writing—review and editing, W.S. and L.W.; visualization, Y.H.; supervision, L.W.; project administration, L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (NSFC) [grant number 42261038] and the Humanities and Social Sciences Program, Ministry of Education, China [grant number 21YJAZH085].

Institutional Review Board Statement

After consideration by the Institutional Review Board of the institution, it was found that the experimental design and protocol of the study were scientifically sound, fair and impartial, posed no harm or risk to the respondents, and protected their rights and privacy, and that the research was free from conflicts of interest and violation of moral and ethical principles and legal prohibitions, and was in accordance with the ethical standards set forth in the Declaration of Helsinki. The Institutional Review Board agreed that the research should proceed as planned. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Jiangxi Agricultural University Science and Technology Ethics Committee (protocol code JXAULL-202250 and date of approval 28 September 2022).

Informed Consent Statement

Informed consent was obtained from all respondents prior to participation in this research.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Theoretical analysis framework.
Figure 1. Theoretical analysis framework.
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Figure 2. Spatial distribution of carbon offsetting participation strength of rural tourism land operators in 100 counties and districts of Jiangxi Province.
Figure 2. Spatial distribution of carbon offsetting participation strength of rural tourism land operators in 100 counties and districts of Jiangxi Province.
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Figure 3. SEM model.
Figure 3. SEM model.
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Table 1. Sample collection of demographic characteristics.
Table 1. Sample collection of demographic characteristics.
QuestionOption
Sex 1. Male; 2. Female
Age1. Under 26 years old; 2. 26–35 years old (inclusive); 3. 36–45 years old (inclusive); 4. 46–60 years old (inclusive); 5. Above 60 years old.
Educational Level1. Primary and below; 2. Junior school 3. High school (including technical secondary school); 4. University (referring to junior college or above).
Annual income from tourism operations1. Less than RMB 50,000; 2. RMB 50,000–99,999; 3. RMB 100,000–200,000; 4. Over RMB 200,000.
Tourism Business Sector1. Catering; 2. Transportation; 3. Shopping; 4. Sightseeing; 5. Accommodation; 6. Entertainment.
Table 2. Meaning of variables and descriptive statistics.
Table 2. Meaning of variables and descriptive statistics.
Latent VariableItemObserved Variable
VPVP1I think participating in carbon offsetting for tourism is good for the ecosystem
VP2I believe that participation in tourism carbon offsets can contribute to the environmental sustainability of tourist destinations
VP3I believe that participation in tourism carbon offsets can eliminate some of the tourism carbon emissions
ACAC1Carbon dioxide emissions contribute to global warming
AC2Excessive carbon emissions from tourism can lead to climate anomalies and ecological damage
AC3Climate anomalies and ecological degradation will affect my tourism operations
ARAR1My business activities generate a certain amount of carbon emissions, which I am responsible for partially offsetting
AR2Carbon emissions from business activities are a direct cause of climate variability
AR3Participating in carbon offsetting for tourism is socially responsible and for a healthier planet
PNPN1Not participating in carbon offsetting for tourism makes me feel guilty
PN2Participation in carbon offsetting for tourism is a personal responsibility
PN3My values inspire me to participate in carbon offsetting for tourism
BABA1I am concerned about ecological issues in tourist destinations and am willing to contribute carbon offsets where I can
BA2I am satisfied with the favorable environmental impact of participating in carbon offsetting for tourism
BA3I am very confident about the future of carbon offsetting in tourism
SNSN1My family and friends support carbon offsetting for travel
SN2I have found that other local tour operators operate to save energy and reduce emissions
SN3Relevant departments will publicize and guide low-carbon business practices
PBCPBC1Tourism carbon offsets are small
PBC2Travel carbon offsetting does not take much of my time
PBC3It is convenient to be able to participate in carbon offsetting for tourism
WTPWTP1I would like to keep an eye on carbon offsetting for tourism
WTP2I would like to participate in carbon offsetting for tourism
WTP3I would like to encourage those in my vicinity to participate in carbon offsetting for tourism
Options: (totally disagree) 1, 2, 3, 4, and 5 (totally agree).
Table 3. Statistical table of demographic variables (n = 746).
Table 3. Statistical table of demographic variables (n = 746).
VariableDescriptionFrequencyProportion
SexMale38050.9%
Female36649.1%
AgeUnder 26 years old253.4%
26–35 years old (inclusive)14519.4%
36–45 years old (inclusive)30440.8%
46–60 years old (inclusive)24633.0%
Above 60 years old263.5%
Educational levelPrimary and below12817.2%
junior school25133.6%
High school (including technical secondary school)24933.4%
University (referring to junior college or above)11815.8%
Annual income from tourism operationsLess than RMB 50,000 17122.9%
RMB 50,000–99,999 20827.9%
RMB 100,000–200,000 24933.4%
Over RMB 200,000 11815.8%
Tourism Business SectorCatering36749.2%
Transportation192.5%
Shopping17523.5%
Sightseeing172.3%
Accommodation9012.1%
Entertainment7810.5%
Table 4. Independent sample T-tests of the willingness to participate in carbon offsetting of operators of three types of rural tourism sites.
Table 4. Independent sample T-tests of the willingness to participate in carbon offsetting of operators of three types of rural tourism sites.
ComparisonTypesSample SizeMeanMean Differencet-Statisticsp-Values
Suburban vs. outer suburbSuburban473.6551−0.3362−1.9630.054 *
outer suburb163.9913−2.193
Suburban vs. remoteSuburban473.6551−0.0817−0.6760.484
remote373.7368−0.703
outer suburb vs. remoteouter suburb163.99130.25451.8540.070 *
remote373.73681.773
Note: * represents a significance level of 10%.
Table 5. Reliability and validity analysis.
Table 5. Reliability and validity analysis.
Latent VariablesItemsMeanStandard DeviationStandardized Factor LoadingComposite ReliabilityAverage Variance ExtractedCronbach’s α
VPVP13.980.8020.8440.8330.6270.830
VP24.000.7660.825
VP33.870.7980.698
ACAC14.030.8210.8660.8820.7150.878
AC24.030.7790.928
AC34.080.7730.730
ARAR13.650.9240.6390.7170.4630.708
AR23.640.8660.802
AR34.070.7470.581
PNPN13.680.7920.8300.7790.5450.774
PN23.800.7340.755
PN33.920.7190.612
BABA13.760.8140.7240.8220.6080.819
BA23.800.7980.863
BA33.800.8010.744
SNSN13.630.8270.8350.8370.6360.831
SN23.580.8410.890
SN33.790.7320.647
PBCPBC13.540.8300.6970.8370.6350.832
PBC23.510.8450.913
PBC33.500.8770.765
WTPWTP13.920.8020.8370.8950.7390.893
WTP23.890.7900.896
WTP33.830.8560.845
Table 6. Inspection of KMO value and Bartlett’s spherical test.
Table 6. Inspection of KMO value and Bartlett’s spherical test.
KMO ValueBartlett’s Spherical Test
Approx. Chi-SquaredDfp-Value
0.93410,806.978276<0.0001
Table 7. Goodness-of-fit model (before and after Bollen–Stine bootstrap correction) and recommended criteria.
Table 7. Goodness-of-fit model (before and after Bollen–Stine bootstrap correction) and recommended criteria.
ItemsBenchmark ValueBefore Bollen–Stine Bootstrap CorrectionAfter Bollen–Stine
Bootstrap Correction
χ2The smaller the better1652.833309.112
DfThe bigger the better242242
Normed Chi-square (χ2/df)1 < χ2/df < 36.8301.277
GFI>0.90.8460.972
AGFI>0.90.8090.963
RMSEA<0.080.0880.019
TLI>0.90.8490.993
CFI>0.90.8680.994
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Song, W.; Wang, L.; He, Y.; Ye, Y.; Jiang, Y. Study on the Strength of Rural Tourism Operators’ Willingness to Carbon Offset and Its Influencing Mechanisms. Sustainability 2024, 16, 6253. https://doi.org/10.3390/su16146253

AMA Style

Song W, Wang L, He Y, Ye Y, Jiang Y. Study on the Strength of Rural Tourism Operators’ Willingness to Carbon Offset and Its Influencing Mechanisms. Sustainability. 2024; 16(14):6253. https://doi.org/10.3390/su16146253

Chicago/Turabian Style

Song, Wei, Liguo Wang, Yan He, Yanting Ye, and Yuting Jiang. 2024. "Study on the Strength of Rural Tourism Operators’ Willingness to Carbon Offset and Its Influencing Mechanisms" Sustainability 16, no. 14: 6253. https://doi.org/10.3390/su16146253

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