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Article

Study on Influencing Factors of Willingness to Pay for Tourism Eco-Compensation in Danxiashan National Geopark

by
Shimin Li
,
Lili Chen
*,
Zhe Chen
,
Xi Deng
,
Jiapeng Huang
and
Yanni Hou
College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(15), 12053; https://doi.org/10.3390/su151512053
Submission received: 19 June 2023 / Revised: 19 July 2023 / Accepted: 4 August 2023 / Published: 7 August 2023
(This article belongs to the Section Environmental Sustainability and Applications)
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Abstract

:
The detrimental effects of tourism on ecosystems are becoming increasingly clear as industries develop rapidly. The relationship between tourism growth and environmental preservation can be balanced through a tourism eco-compensation mechanism, which is a key institutional mechanism. The academic community has become a popular scientific topic. Developing a tourism eco-compensation mechanism can be based on knowledge of the public’s willingness to pay for it and the variables that influence it. This paper is based on the theory of planned behaviour and uses the Danxiashan National Geopark as an example. A questionnaire was used to gather data for this study. SPSS (version 26.0) was then used to analyse the data and determine how variables affect the WTP for ecotourism work. The findings demonstrate that environmental values and sensitivity are the key factors influencing the willingness to pay for tourism eco-compensation. This study concludes by offering solutions and ideas for building an eco-compensation mechanism for tourism.

1. Introduction

The detrimental effects of tourism on ecosystems are becoming increasingly clear as the industry develops rapidly. As a result, awareness of ecotourism has increased. Ecological compensation mechanisms have drawn increasing attention as a viable solution to the conflict between economic growth and ecological protection, and they include numerous application-related components. Tourism eco-compensation refers to the tourist industry’s response to the development of ecotourism to protect the ecosystem on which it relies. This effective method of market-based ecological compensation coordinates the growth and protection of the protected areas [1].
The tourism resources of geoparks are primarily non-renewable geological remnants, as opposed to other kinds of natural parks, such as marine, forest, and wetland parks. The Danxiashan National Geopark is a world natural heritage and national geological park. It bears the major responsibilities of environmental protection, geological heritage protection, and popular science education. However, there are many issues with the ecological protection of the Danxiashan National Geopark as a result of the increasing level of tourism development, including prioritizing development over protection and the destruction of the environment and geological artifacts due to tourists’ uncivilized behaviour. Giving full play to the role of the tourism ecological compensation mechanism is necessary to achieve sustainable development of the Danxiashan National Geopark, resolve the conflict between tourism development and ecological environment protection, and promote the sustainable development of the Danxiashan National Geopark tourism. Additionally, creating tourism eco-compensation mechanisms is crucial for the long-term sustainability of national parks.
Based on the theory of planned behaviour, this study takes the Danxiashan National Geopark as the research object and introduces environmental values (EVs) and environmental sensitivity (ES) as the important factors affecting willingness to pay (WTP) for tourism ecological compensation, which provides a new theoretical basis and research perspective for studying the factors that influence the WTP for tourism ecological compensation. Subsequently, this study explores the public’s WTP for tourism eco-compensation and the influencing variables using the Danxiashan National Geopark as the research subject. Additionally, it proposes remedies and ideas for building a tourism eco-compensation mechanism in the Danxiashan National Geopark, in order to provide reference for the construction of tourism eco-compensation mechanisms in other nature reserves.

2. Previous Studies

2.1. Tourism Eco-Compensation

The term tourism eco-compensation refers to the institutional framework for safeguarding the ecosystem upon which the tourism system depends, promoting the sustainable growth of tourism, internalising the external costs associated with the creation of related tourism resources, and balancing the interests of various stakeholders involved in tourism-related activities [2]. Tourism eco-compensation in China is a relatively new research area. The study of tourist ecological compensation in China began very recently. The practice of tourism ecological compensation developed earlier than the theoretical research [3]. These studies have focused on the definition of subject and object [2,4,5,6], compensation standards [4,5,7], mechanism construction [5,7,8,9,10,11,12,13], and the connotation of tourism eco-compensation [14,15,16,17,18]. Additionally, research in China is also often macro- and government-driven. While the research objects have primarily involved forests, oceans, and national parks at home and abroad, the research participants have lacked diversification. Subsequently, scholars have ignored other ecologically fragile tourist areas that require attention. Researchers have determined tourism eco-compensation standards by evaluating respondents’ WTP but have ignored other forms of eco-compensation. When studying tourism eco-compensation standards, scholars have paid attention to the factors that affect the WTP and conducted in-depth research [10,13,19,20,21].
The WTP for tourism eco-compensation refers to the willingness of the compensation subject to compensate for the compensation object through capital, labour, intelligence, technology, and other forms [22]. However, research on the factors influencing the WTP for tourism eco-compensation is insufficient. Most scholars use correlation analysis and regression methods, such as logistics and probit methods, to discuss the demographic characteristics, policies, situations, and other factors of the respondents, ignoring the influence of subjective cognition, psychological factors, cultural differences, and other factors on the WTP for tourism eco-compensation. Currently, scholars’ research on the factors affecting the WTP for ecological compensation were not combined with sociology, economics, and psychology. Therefore, they could not further explore the influencing factors.

2.2. Factors Affecting the WTP for Tourism Eco-Compensation

According to the theory of planned behaviour, this study argues that attitudes toward the behaviour (AB), subjective norms (SNs), and perceived behavioural control (PBC) are factors affecting the public’s WTP for tourism eco-compensation. However, the theory is still constrained by the ‘rational person’ hypothesis. In real life, an individual’s behaviour is not completely controlled by rationality but is also affected by the individual’s subjective psychology. For example, the public’s WTP for tourism eco-compensation in the Danxiashan National Geopark is influenced by both rationality and sensibility. Moreover, many scholars have confirmed the impact of psychological factors on individuals’ WTP for ecological compensation. Obeng et al. [23] argued that attitudes, demographics, and psychosocial variables should be fully considered when predicting WTP and designing market-based protection actions. Tamara et al. [24] demonstrated that respondents’ attitudes toward ecosystem services significantly impact their WTP. They suggested that psychological differences should be considered when designing and legalising EU agricultural environment and protection policies. Li et al. [25] argued that residents’ WTP depends on external conditions, realistic abilities, and psychological characteristics.
However, values differ from attitudes because attitudes are clearer, more micro, and directed toward specific objects [26]. These values are relatively macroscopic and act on the entire system. Yu [27] confirmed that resident environmental values (EVs) significantly impact environmental attitudes. According to Stern [28], EVs significantly predict environmental conduct. Several academics have also verified that EVs significantly influence behaviour. Therefore, this study concludes that EVs influence the WTP for tourism eco-compensation.
The theory of interpersonal behaviour distinguishes emotions from attitudes, and an increasing number of scholars have independently studied the formation mechanism of environmental behaviour at the emotional level. Researchers have also demonstrated the impact of environmental sentiment, or environmental sensitivity (ES), on individual willingness and behaviour, and it is considered an important factor in promoting residents’ pro-environmental behaviour [29]. Therefore, this study argues that ES is one of the factors influencing the WTP for tourism eco-compensation.
In summary, it is hypothesised that AB, SNs, PBC, EVs, and ES are the influencing factors of the WTP for tourism eco-compensation. In this way, the theoretical framework of this paper is constructed (Figure 1).

3. Methods

3.1. Present Situation of Eco-Compensation in the Danxiashan National Geopark

The Regulations on the Protection and Management of the Danxia Mountain in Guangdong Province points out that all mountain forests in Danxiashan are listed as provincial ecological public welfare forests for protection. This means that all mountain forests within 292 square kilometres of Danxiashan are provincial ecological public welfare forests. Since 1999, the ecological public welfare forest within the scope of the Danxiashan Heritage Site has begun to be compensated. In 2012, the leading group on the definition and division of ecological public welfare forests and the distribution of ecological benefit compensation in the Danxiashan Scenic Area of Huangkeng Town was established, and the work on the delineation and compensation of ecological public welfare forests was more refined. In 2021, the benefit compensation standard of ecological public welfare forests above the provincial level in Guangdong Province will be increased from an average of 40 CNY/mu to 42 CNY/mu, and the compensation of ecological public welfare forests will be directly distributed to forest farmers in the form of cash. In order to solve some problems left over by history, based on the long-term development of Danxiashan, and taking full account of the vital interests of forest farmers, Danxiashan also redeems and transfers artificial forests. In 2020, the artificial forest redemption and transfer project in the Danxiashan Scenic Area will spend CNY 18.1203 million. According to the Regulations on the Protection and Management of Danxiashan in Guangdong Province, anyone who uses the geological relics and scenic resources of Danxiashan for business activities shall pay the paid use fee of geological relics and scenic resources in accordance with the regulations. Ticket revenues, geological heritage use fees, and paid use fees for scenic resources are collected by the Danxiashan management agency, and the two-line management of revenue and expenditure is implemented for the protection, management, and infrastructure construction of Danxiashan. In 2020, the expenditure of the Danxia Mountain protection and management project was CNY 3.5031 million.
At present, there is no clear tourism ecological compensation mechanism in the Danxiashan National Geopark. Many contradictions between tourism development and ecological environment protection cannot be effectively solved, which hinders the sustainable development of Danxiashan to a certain extent. The geological resources of Danxiashan are non-renewable, which makes it more necessary to pay attention to the protection in the development. The tourism ecological compensation mechanism is an effective means to reconcile the contradiction between protection and development.

3.2. Survey Development

The subjects of this study were the general public, including tourists who have been to the Danxiashan National Geopark and citizens who have not been to the Danxiashan National Geopark. This study primarily obtained relevant data through online questionnaire surveys. The questionnaire comprises items explaining the variables of each influencing factor. The public’s WTP for tourism eco-compensation was designed based on previous research results and the research situation.
The first part of the questionnaire was a survey of the respondents’ WTP for tourism eco-compensation, which included “WTP for tourism eco-compensation (WTP1)”, “WTP for money (WTP2)”, and “willingness to participate in environmental protection behaviour (WTP3)”, where WTP1 is the total willingness, including WTP2 and WTP3.
The second part of the questionnaire was the scale test question, which measured the respondents’ levels of AB, SNs, PBC, EVs, and ES using a five-point Likert scale (Table 1). This part of the scale refers to previous research results and is designed in the Chinese context.
The third part of the questionnaire included the demographic characteristics of the respondents, including their sex, age, origin, education, monthly income, and occupation.

3.3. Data Collection

Through a small-scale pre-survey, the rationality of the questionnaire design and accuracy of the item content were verified. Combined with the feedback received from the questionnaire and the results of the sample data analysis, the questionnaire was modified to form a formal questionnaire. From 16–20 March 2022, the official online questionnaire was distributed through SurveyMonkey. Finally, 412 questionnaires were collected. After eliminating invalid questionnaires with too short answer times and consistent options, 391 valid questionnaires were obtained, with an effective rate of 94.90%.

3.4. Data Analysis

3.4.1. Reliability and Validity Test

SPSS (version 26.0) was used to test the reliability and validity of the questionnaire data. The results demonstrated that Cronbach’s alpha coefficients of all variables were greater than 0.6. The Cronbach’s alpha coefficient of the entire questionnaire was 0.857, which indicated that the results of this questionnaire were reliable. The KMO values of all variables were greater than 0.6, and the Bartlett sphericity test p-values were all significant, demonstrating that the sample was suitable for a factor analysis. A principal component analysis was used to conduct an exploratory factor analysis of the items of each variable, and the extraction number of factors was specified as 5. The maximum variance method was used for the rotation. According to the results of the exploratory factor analysis and reliability analysis, some items were deleted to improve the explanatory power and reliability of the scale. Finally, six items were deleted. After deleting the items, the KMO values of all variables were greater than 0.6 and the Bartlett sphericity test p-values were significant. An exploratory factor analysis revealed that each item could be classified into corresponding components. The factor load was between 0.512 and 0.817, which was greater than 0.5. The total variance interpretation rate of the scale was 63.42%, indicating that the structural validity was good.

3.4.2. Sample Characteristics and Variable Description

There were more female respondents (63.4%) than male respondents in the sample. In terms of age composition, respondents aged 18–25 years (44.8%) were the largest group, followed by 26–30 years (22%) and 31–40 (21%). Owing to the distribution of this questionnaire through the network, there were fewer samples under 18 years and over 50 years old. Most respondents were from Guangdong Province (64.5%). The respondents generally had a high level of education, and undergraduates, junior colleges, and above accounted for 77%. In terms of occupational composition, enterprise personnel (34.3%) and students (27.4%) accounted for the highest proportion, followed by civil servants and public institutions. In terms of the monthly income distribution, CNY 2001–5000 (27.9%) and CNY 5001–8000 (26.9%) accounted for the most, followed by below CNY 2000 (22.8%).
The descriptive statistical results of the respondents’ WTP for tourism eco-compensation in the Danxiashan National Geopark and their AB, SNs, PBC, EVs, and ES are illustrated in Table 2.

3.4.3. Correlation Analysis

To determine the correlation between the variables and provide a basis for the next regression analysis, a bivariate correlation analysis was conducted on the AB, SNs, PBC, EVs, ES, and the WTP for tourism eco-compensation. The results of the analysis are presented in Table 3. The results demonstrate that AB, SNs, PBC, and ES significantly correlate with the WTP for tourism eco-compensation. Only EVs significantly correlate with the willingness to participate in environmental protection behaviour.

4. Results

Linear regression analysis is an analysis method used to study the influence of an independent variable X on a dependent variable Y. The independent variable X can be one or more. The stepwise regression analysis method is usually used to establish the optimal or appropriate regression model. The basic idea is to introduce all independent variables into the model one by one. If the selected variables become insignificant after the introduction of new variables, they will be eliminated. If the variables that are eliminated after the introduction of new variables become significant, they are re-selected into the model. This method can retain the most significant important variables, and can eliminate non-significant variables, with high prediction accuracy [44]. Based on the results of the correlation analysis in the previous section, this section will use the multiple stepwise regression analysis method to analyse the influence and explanation of each factor on the willingness to carry out tourism ecological compensation, the willingness to pay money, and the willingness to participate in environmental protection.

4.1. Regression Analysis of Tourism Eco-Compensation Willingness

Taking tourism eco-compensation willingness as the dependent variable, a multiple-stepwise regression analysis was conducted. The results are summarised in Table 4. The order of each influencing factor in the regression model was SNs > AB > ES. PBC was not selected in the regression model, indicating that it had no significant impact on tourism eco-compensation willingness. SNs (B = 0.232, p < 0.001), AB (B = 0.185, p < 0.01), and ES (B = 0.127, p < 0.05) had significant impacts on tourism eco-compensation willingness. This indicates that the more the public is influenced by the people around them, the more positive their attitudes toward tourism eco-compensation, the stronger their ES, and the stronger their willingness to engage in tourism eco-compensation.

4.2. Regression Analysis of Monetary WTP

A multivariate stepwise regression analysis was performed using the monetary WTP as the dependent variable. Table 5 presents the results of the analysis. The order of the factors in the regression model was AB > SNs > PBC. ES was not selected in the regression model, indicating that its impact on the monetary WTP intention was not significant. AB (B = 0.318, p < 0.001), SNs (B = 0.237, p < 0.001), and PBC (B = 0.208, p < 0.001) had significant impacts on WTP. This indicates that the more active the public’s attitudes toward tourism eco-compensation, the greater the influence of the people around them, the easier it is to perceive monetary payments, and the stronger their monetary WTP.

4.3. Regression Analysis of Willingness to Participate in Environmental Behaviour

A multiple stepwise regression analysis was performed using the willingness to participate in environmental behaviour as the dependent variable. Table 6 presents the results of the analysis. The order of the influencing factors in the regression model was AB, EVs, and ES. SNs and PBC were not selected in the regression model, indicating that SNs and PBC had no significant impact on the willingness to participate in environmental behaviour. EVs (B = 0.251, p < 0.001), AB (B = 0.248, p < 0.001), and ES (B = 0.180, p < 0.001) had a significant impact on the willingness to participate in environmental behaviour. This indicates that the more positive the public’s EVs, the more positive the attitudes toward tourism eco-compensation, the stronger the ES, and the stronger the public’s willingness to participate in environmental behaviour.

5. Conclusions and Countermeasure

5.1. Conclusions

(1)
The WTP for tourism eco-compensation in this study includes three aspects: tourism eco-compensation willingness, monetary WTP, and the willingness to participate in environmental behaviour. The public’s WTP for tourism eco-compensation was high, with 93.86% of the 391 respondents willing to carry out tourism eco-compensation when visiting the Danxiashan National Geopark. The public is more willing to pay for tourism eco-compensation by participating in environmental protection activities than through monetary payments. It is feasible to establish a tourism ecological compensation mechanism with universal participation.
(2)
SNs, AB, and ES have a significant positive impact on the willingness to conduct tourism ecological compensation. SNs (B = 0.232, p < 0.001) have the greatest influence, followed by AB (B = 0.185, p < 0.01), and finally ES (B = 0.127, p < 0.05), indicating that the greater the influence of the people around the public, the more positive the attitudes toward tourism ecological compensation, the stronger the environmental sensitivity, and the stronger the willingness to carry out tourism ecological compensation.
(3)
AB, SNs, and PBC have a significant positive impact on the willingness to pay for money. AB (B = 0.318, p < 0.001) had the greatest impact, followed by SNs (B = 0.237, p < 0.001), and finally PBC (B = 0.208, p < 0.001). This shows that the more positive the public’s attitudes toward tourism ecological compensation, the greater the influence of the people around them, the easier it is to perceive monetary payment, and the stronger their willingness to pay money.
(4)
EVs, AB, and ES have a significant positive impact on the willingness to participate in environmental behaviour. EVs (B = 0.251, p < 0.001) had the greatest impact, followed by AB (B = 0.248, p < 0.001), and finally ES (B = 0.180, p < 0.001), indicating that the more positive the public’s environmental values, the more positive the attitudes toward tourism ecological compensation, the stronger the environmental sensitivity, and the stronger their willingness to participate in environmental protection behaviour.

5.2. Countermeasure

5.2.1. Multi-Support, Establish a Comprehensive Compensation Mechanism

Tourism eco-compensation is an extension of the ecological compensation in tourism. Compared with ecological compensation, tourism eco-compensation has stronger marketability. It can perform ecological compensatory work more effectively by using market mechanisms. Constructing a market-oriented tourism eco-compensation can reconcile the contradictions between protection and development. Presently, the Chinese tourism eco-compensation mechanism is basically in the form of financial compensation to carry out ecological compensation and the lack of participation in the form of intelligence and labour. Therefore, it is necessary to develop a diversified tourism eco-compensation mechanism.
First, a scenic spot can tap into the cultural heritage and personality characteristics of a scenic area to create distinctive ecological public welfare products. Second, to explore the implementation plan of a special tax on tourism eco-compensation, the government can take the Danxiashan National Geopark as a pilot to implement a special tax on tourism eco-compensation, drawing on the advanced experience of levying tourism taxes in other countries. For instance, New Zealand charges visa costs to visitors as an environmental tourism tax, and Spain’s Balearic Islands charge visitors from other countries an “ecological tax” to support local environmental protection. Third, by learning from relevant experiences at home and abroad, the government and other institutions can establish the Danxiashan Tourism Eco-Compensation Foundation according to local conditions, which can effectively manage social donations and funds raised in the market through the foundation. Fourth, according to the requirements and difficulties of the activities, various levels, and forms of tourism eco-compensation, environmental protection activities can be conducted to attract different levels of public participation in tourism eco-compensation.

5.2.2. Public Participation, Strengthening Public Willingness to Compensate

The results of the empirical analysis demonstrate that the public is positively influenced by the concept of ecological civilisation construction in China. Their EVs were more positive, and their WTP for tourism eco-compensation was higher. This provides a solid foundation for implementing a tourism eco-compensation mechanism with the participation of the entire population. First, the government should establish a tourism eco-compensation mechanism with the participation of the entire population and expand its radiation effect to attract potential tourists to become tourists, attract tourists to become revisit tourists, and achieve environmental protection and economic development in a win–win situation. Second, the government and scenic spots should increase the publicity of knowledge related to the tourism eco-compensation mechanism and strengthen the public’s moral education regarding the tourism eco-compensation mechanism, clarify tourists’ responsibilities as beneficiaries, and create a situation conducive to public participation in the tourism eco-compensation mechanism. Third, from a social perspective, the government should implant a new-age ecological concept into the personal value system through government guidance, community education, school education, institutional constraints, and other interlocking measures [8], to establish the public’s positive environmental values and attitudes toward the behaviour and cultivate strong ES.
Regarding scenic spots, the Danxiashan National Geopark should build and maintain an emotional connection between nature reserves and tourists to improve tourists’ environmental emotions toward it. The public understanding of the ecological environment should be improved through environmental and natural education. It should also guide the public to appreciate the beauty of nature and cultivate a love for it. This then improves the public’s WTP for tourism eco-compensation.

5.2.3. Openness and Transparency, Improve Oversight and Oversight Channels

Currently, much of the ecological compensation in China focuses on the compensation of ecological public welfare forests and forest land expropriation. Many ecological compensations disclose only the total compensation. However, the specific uses and destinations of ecological compensation have not yet been disclosed. The public does not know how much ecological compensation is used to protect the environment or how to use it to protect the environment. Consequently, an open, transparent ecological tourism compensation mechanism is required. First, the Danxiashan National Geopark should clarify the composition of tickets and the specific use and proportion of ticket income. It should explain the proportion of ticket income used for tourism eco-compensation and eliminate tourist resistance. It should make public the source and specific direction of tourism eco-compensation and clarify that each eco-compensation is used to protect the ecological environment. It should publicly announce the source and specific direction of tourism eco-compensation and clarify that each tourism eco-compensation is used for ecological environment protection. It should also supervise and manage subject and object compensation. Third, governments, scenic spots, and related environmental protection organisations should establish relevant tourism eco-compensation and punishment systems.

6. Discussion

Little academic research has been conducted on the subjective psychological factors affecting the WTP for tourism eco-compensation. Based on the theory of planned behaviour, this study introduces EVs and ES as important factors affecting the WTP for tourism eco-compensation from the perspective of the public’s subjective psychological vision, which provides a new theoretical basis and research perspective for studying the influencing factors of the WTP for tourism eco-compensation. Investigating the public’s WTP for tourism eco-compensation in the Danxiashan National Geopark is of great practical significance for the resource protection of national geoparks and the public’s decision making to improve their sustainable development. Exploring the factors influencing the WTP for tourism eco-compensation and clarifying the mechanism of each influencing factor can provide a theoretical foundation for future public understanding of tourism eco-compensation. This could help effectively alleviate the contradiction between people and land caused by tourism development and help to realise the sustainable development of tourism in nature reserves. Finally, this study provides a reference for constructing tourism eco-compensation mechanisms for nature reserves. There are still some restrictions in this study because of the limitations of objective factors: there are not enough samples. The mechanism of the influencing factors of the WTP for tourism ecological compensation is only examined by descriptive analysis, correlation analysis, regression analysis, and other statistical analysis methods; the effect path of each factor is not thoroughly researched. Future studies might combine other approaches, including employing a structural equation model to examine the influence path in more detail.

Author Contributions

Writing—original draft and methodology, S.L.; writing—review and editing, L.C.; software and validation, Z.C., X.D., J.H. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 12053 g001
Figure 1. The research model.
Figure 1. The research model.
Sustainability 15 12053 g001
Table 1. Definition and measurement of variables.
Table 1. Definition and measurement of variables.
VariableMeaning of VariableMeasurement ItemsSource of Variables
Attitudes toward the behaviour (AB)Personal evaluation of the degree of approval or disapproval of the implementation of tourism ecological compensation.AB1 Tourism ecological compensation can improve the ecological environment Ajzen [30]
Xiong et al. [31]
Fielding et al. [32]
AB2 It is necessary to implement the tourism ecological compensation policy
AB3 Paying tourism ecological compensation is pleasant
AB4 It is wise to pay for tourism ecological compensation
Subjective norms (SNs)The personal perceived social pressure of paying or not paying tourism ecological compensation in the Danxiashan National Geopark comes from important others or groups.SN1 People around me will be willing to carry out tourism ecological compensationHan et al. [33]
Fielding et al. [32]
Zhang and Wang [34]
Sun [35]
SN2 People around me want me to carry out tourism ecological compensation
SN3 If I make tourism ecological compensation, people around me will appreciate my behaviour
Perceived behavioural control (PBC)Individuals predict the perception of the difficulty of paying for tourism ecological compensation by measuring their ability to control external factors.PBC1 For me, it is difficult to pay for ecological compensationHan et al. [33]
Zou and Chen [36]
Fei [37]
PBC2 I have the ability to pay for ecological compensation
PBC3 I have time to participate in the environmental behaviour of ecological compensation
PBC4 I have the energy to participate in the environmental behaviour of ecological compensation
PBC5 I have knowledge of ecological compensation
Environmental values (EVs)Individual’s values on the environment and related issues.EV1 Protecting the environment will reduce employment opportunitiesStern [28]
Gagnon Thompson and Barton [38]
He [39]
Wang and Zhou [40]
EV2 The code of conduct for the protection of the environment limits my personal choice and freedom
EV3 Environmental pollution will endanger not only our generation, but also the next generation
EV4 Environmental pollution is more harmful to humans than we realize
EV5 Humanity should respect nature and live in harmony with it
EV6 The Earth itself is valuable, and its value is not given by humans
Environmental sensitivity (ES)The degree to which an individual can feel, appreciate, and care about the environment is an intrinsic emotional trait of an individuals’ recognition of environmental values.ES1 I focus on environmental issues in current news Bamberg [41]
Hsu and Roth [42]
Yu [37]
Qian et al. [43]
ES2 I focus on the environmental events around me
ES3 I will discuss related environmental issues
ES4 I will be angry when I see the destruction of the environment and resources when carrying out tourism activities
ES5 I focus on the geological heritage protection activities of the Danxiashan National Geopark
Table 2. Descriptive statistical results of the research variables.
Table 2. Descriptive statistical results of the research variables.
VariableItemMean ValueStandard Deviation
Willingness to pay for tourism eco-compensation (WTP)WTP13.750.883
WTP23.500.931
WTP34.110.846
Attitudes toward the behaviour (AB)AB13.900.823
AB23.930.921
AB43.790.881
Subjective norms (SNs)SN13.560.851
SN23.620.844
SN33.760.852
Perceived behavioural control (PBC)PBC33.600.966
PBC43.570.894
PBC53.360.966
Environmental values (EVs)EV34.250.901
EV44.070.918
EV54.230.827
EV64.200.817
Environmental sensitivity (ES)ES13.800.774
ES23.830.78
ES33.710.801
ES53.530.949
Table 3. Results of the correlation analysis.
Table 3. Results of the correlation analysis.
VariableABSNsPBCEVsESWTP1WTP2WTP3
AB1
SNs0.531 **1
PBC0.395 **0.531 **1
EVs0.397 **0.146 **0.0191
ES0.494 **0.530 **0.590 **0.258 **1
WTP10.371 **0.398 **0.303 **0.0470.342 **1
WTP20.526 **0.516 **0.459 **0.0840.386 **0.493 **1
WTP30.436 **0.327 **0.227 **0.396 **0.367 **0.361 **0.205 **1
** p < 0.01.
Table 4. Stepwise regression analysis results of the factors on tourism eco-compensation willingness.
Table 4. Stepwise regression analysis results of the factors on tourism eco-compensation willingness.
Dependent VariableModelVariableNon-Standardised CoefficientStandardised CoefficienttSig.R2
BStandard ErrorBeta
Tourism eco-compensation willingness1(constant)1.8510.226 8.2030.0000.158
SNs0.5200.0610.3988.5440.000
2(constant)1.3110.257 5.1060.0000.194
SNs0.3650.0700.2795.1860.000
AB0.2850.0690.2234.1400.000
3(constant)1.0510.280 3.7560.0000.204
SNs0.3030.0750.2324.0330.000
AB0.2370.0720.1853.3030.001
ES0.1810.0800.1272.2780.023
Table 5. Stepwise regression analysis results of the factors on monetary willingness to pay.
Table 5. Stepwise regression analysis results of the factors on monetary willingness to pay.
Dependent VariableModelVariableNon-Standardised CoefficientStandardised CoefficienttSig.R2
BStandard ErrorBeta
Monetary willingness to pay1(constant)0.7520.229 3.2860.0010.277
AB0.7100.0580.52612.2090.000
2(constant)0.0100.242 0.0430.9660.355
AB0.4740.0650.3517.2930.000
SNs0.4550.0660.3306.8540.000
3(constant)−0.2240.243 −0.9240.3560.385
AB0.4300.0640.3186.6840.000
SNs0.3260.0710.2374.5830.000
PBC0.2490.0570.2084.3670.000
Table 6. Stepwise regression analysis results of the factors on willingness to participate in environmental behaviour.
Table 6. Stepwise regression analysis results of the factors on willingness to participate in environmental behaviour.
Dependent VariableModelVariableNon-Standardised CoefficientStandardised CoefficienttSig.R2
BStandard ErrorBeta
Willingness to participate in environmental behaviour1(constant)2.0390.220 9.2590.0000.190
AB0.5350.0560.4369.5670.000
2(constant)1.1000.272 4.0430.0000.249
AB0.4070.0590.3326.9170.000
EV0.3430.0620.2645.5130.000
3(constant)0.6590.295 2.2370.0260.274
AB0.3040.0650.2484.7150.000
EV0.3260.0620.2515.2990.000
ES0.2450.0680.1803.5940.000
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Li, S.; Chen, L.; Chen, Z.; Deng, X.; Huang, J.; Hou, Y. Study on Influencing Factors of Willingness to Pay for Tourism Eco-Compensation in Danxiashan National Geopark. Sustainability 2023, 15, 12053. https://doi.org/10.3390/su151512053

AMA Style

Li S, Chen L, Chen Z, Deng X, Huang J, Hou Y. Study on Influencing Factors of Willingness to Pay for Tourism Eco-Compensation in Danxiashan National Geopark. Sustainability. 2023; 15(15):12053. https://doi.org/10.3390/su151512053

Chicago/Turabian Style

Li, Shimin, Lili Chen, Zhe Chen, Xi Deng, Jiapeng Huang, and Yanni Hou. 2023. "Study on Influencing Factors of Willingness to Pay for Tourism Eco-Compensation in Danxiashan National Geopark" Sustainability 15, no. 15: 12053. https://doi.org/10.3390/su151512053

APA Style

Li, S., Chen, L., Chen, Z., Deng, X., Huang, J., & Hou, Y. (2023). Study on Influencing Factors of Willingness to Pay for Tourism Eco-Compensation in Danxiashan National Geopark. Sustainability, 15(15), 12053. https://doi.org/10.3390/su151512053

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