Assessing Household Willingness to Pay for the Conservation of the Phou Chom Voy Protected Area in Lao PDR

Abstract

This study aimed to examine whether local residents were willing to pay (WTP) for the improvement of the Phou Chom Voy Protected Area (PCV PA), by using the hypothetical scenario framework of the contingent valuation method. We interviewed a sample of 365 local residents. Among the respondents, 271 were willing to pay to maintain the protected area. Most residents who refused to pay had low incomes and stated that they did not want to increase their monthly expenses by contributing to the conservation fund. The estimated mean willingness to pay among the respondents who expressed the maximum willingness to pay for natural forest conservation because of its natural value and attractiveness for tourism was Lao Kip (LAK) 27,055/year. The aggregate willingness to pay for the protected area improvement was approximately LAK 93 million. The logit regression results revealed that age, education level, annual household income, and attitude toward conservation significantly influenced willingness to pay. The results of this study provide insights into applying willingness to pay in sustainable financing, to develop market-based conservation approaches in protected areas, thus reducing ongoing biodiversity losses and maintaining natural resources.

1. Introduction

“Protected areas” (PAs) are clearly defined geographical locations that are recognized, devoted, and managed, either legally or by other effective measures, for long-term nature conservation, including ecological services and cultural values [1]. Many PAs are also managed to reduce poverty and encourage sustainable development [2]. The forested area of PAs worldwide has increased by 191 million hectares since 1990 and has recently been estimated at 726 million hectares [3]. However, many PAs lack financial stability and, accordingly, cannot accomplish conservation or development goals [2]. While approximately 12% of PAs are established, only 25–30% of them are under active management. Notably, the rapid increase in the number of PAs has limited core funding [4].

This study focused on a protected area in Lao PDR. Laos is broadly located in the Asia-Pacific region, where the majority of tropical forests and wild animal habitats can be found. This region has 35,475 PAs; however, only 2825 of them have been subject to management effectiveness evaluations [5]. Southeast Asian countries, including Laos, Thailand, Vietnam, Cambodia, and Myanmar, are home to nearly 15% of the world’s tropical forests and are rich in biodiversity [6]. Southeast Asia has approximately 607 PAs, which comprise approximately 395,768 km² [5].

Laos is one of Southeast Asia’s least-developed and natural-resource-rich countries. It is mainly rural, and its residents’ livelihoods directly depend substantially on natural resources [7]. In 1993, the government established protected areas, which covered approximately 43,220 km² or 18.69% of the nation’s terrestrial and inland-water protected areas, including three national parks, 20 national PAs, one Association of Southeast Asian Nations heritage park, three provincial protected areas, one hunting reserve, and two Ramsar sites. These PAs were formed to maintain natural resources, including plants, aquatics, wildlife species, forest ecosystems, and historical, cultural, and touristic sites, and to use for educational purposes [8]. While PAs are primarily managed by the government, PA funding has been decreasing [9]. PAs impacted by funding cuts are not expected to provide social, economic, and cultural benefits. In addition to biodiversity preservation [10], insufficient conservation financing contributes to ecosystem service market failure, owing to free-rider products and services and a lack of financial transparency in assessing ecosystem-related goods and services [11]. Many PAs in Laos are provided between USD 1000 to USD 5000 of government funding per year; however, a few PAs are not given any funding or other resources from the government [12]. Funding constraints limit the ability of PA managers to implement continuous conservation activities to externalities and, to date, no alternative financial models have been deployed [13].

Many biodiversity and environmental services are not valued and do not have markets that align with the primary principle of PA value assessment. Therefore, they are rarely considered in monetary or profit-and-loss calculations, which influence how people choose to generate, trade, and invest. Additionally, PA degradation and destruction remain primarily unaccounted for; they tend to be low priorities in finance and policy, compared to sectors considered productive in development terms [14].

Determining the goods and services provided by PAs and measuring their values is not always easy. Many commodities and services are not sold commercially; therefore, their market values are unknown and must be assessed and valued in monetary terms [15].

The contingent valuation method (CVM) is widely used to estimate the economic value for various natural resources and environmental commodities that are not traded and have no market price [16]. This method can be used to estimate both use and non-use values and is the most commonly used method for determining non-use values. This is also the most persuasive non-market value approach. Specifically, the CVM asks people to express their willingness to pay (WTP) for a hypothetical scenario and defined environmental services [17].

The reliability and validity of the results acquired through the CVM are limited due to the following biases that can occur in its use. First, when asked about their hypothetical WTP, respondents tend to give higher values than what they would pay in an actual situation. Second, when expressing the value of a good or service, respondents may sometimes be expressing their feelings about the scenario or the valuation exercise; here, again, they may give higher values than what they would in an actual situation, because they do not believe that the change described in the hypothetical scenario is realistic. Third, depending on the type of payment chosen, respondents may express varying levels of WTP (e.g., if the form of payment is a voluntary donation, then respondents may give higher values than if they were asked to pay through higher taxes); here, strategic bias occurs when a respondent attempts to provide a valid response in order to influence a specific outcome, such as the provision of a good. Last, non-response bias is a concern because people who do not participate in the survey are more likely to have different values than people who do participate [16].

However, the CVM remains popular in the context of valuing ecosystem services. For example, there was a contingent valuation study in Ethiopia to estimate local users WTP for the protection and conservation of the Wof-Washa Forest in the North Shewa Zone of the Amhara National State, as well as to identify factors affecting household WTP [18]. Open-ended questions were used to randomly survey 353 households. Gender, age, education level, income, bid price, and distance from the conservation area proved to be crucial parameters for determining a household’s WTP [18].

Meanwhile, in Nepal, there was an investigation of the economic value of ecosystem services in protected areas, namely the Baghmara Buffer Zone Community Forest. The study relied on a contingent value survey of 95 visitors to protected areas and 100 tourists. The findings suggest that the average WTP for recreational and aesthetic services was approximately USD 460 per user per year, totaling USD 3,806,468 per year for all users [2].

Many organizations, such as those in the health, transportation, and environment sectors, have employed economic value in their work. Furthermore, due to diverse interest groups, corporations, governments, and scholars seeking economic valuations of environmental products, the use of valuation methodologies has expanded [19]. Environmental resource valuation is a crucial strategy for maximizing the advantages gained from natural ecosystem services for international, regional, national, and local benefits. Market methods have been widely employed worldwide to assess environmental goods and services using monetary instruments, and users have access to economic data [19,20]. Ecosystem services provided and consumed in invisible market transactions have long been considered in economic assessments of the environment [21].

The monetary prices of ecosystem services also serve as a guideline for user preferences and the relative value that current generations place on them. Consider a case in which ecosystem services enhanced timber production by USD 50. In this scenario, beneficiaries of the service should be willing to pay up to USD 50 [22]. These values assist in resource allocation decisions for alternative use. It should be recognized that monetary values based on market prices almost always ignore future generations’ rights values [17].

According to the UN Food and Agriculture Organization (FAO), over 13 million hectares of the world’s forests are lost annually due to deforestation. Between 2000 and 2005, the net annual loss of forest area was 7.3 million hectares (0.18% of the world’s total forest area).

Deforestation, fragmentation, and degradation are caused by several factors that may be direct or indirect. Human attributes are the most important elements linked to the degradation of forest ecosystems and biological variety. Forest conversion activities, such as agriculture, overgrazing, uncontrolled shifting cultivation, infrastructure development (e.g., road construction, hydroelectric development, urban sprawl), mining and oil exploitation, forest fires, pollution, and climate change, have varying effects on forest biological diversity. Furthermore, as forests deteriorate, so does their biological diversity. Forest ecosystems have become less resilient as a result of this degradation, making it more difficult for them to adapt to changing environmental conditions [23].

Similar to the challenges mentioned above, the forest and forestland in the PA have been disturbed for many years, and this issue is likely to continue in the future owing to several diverse causes. Unfortunately, the alternative mitigation necessary to address those threats is lacking. Research that examines users’ WTP for protection and factors affecting their contribution are also insufficient for policymakers. Therefore, this study aims to fill the gap by providing a protected area baseline economic value and scientific evidence to support protected area institutions for better decision-making. Furthermore, exploring WTP for protective activities among users of PA ecosystem services may offer insights into best practices for funding PAs moving forward. However, scholars have not yet explored WTP for protective activities among users of PA ecosystem services, the factors affecting this WTP, or the related economic value. Moreover, in Laos, the current system for setting user fees does not depend on consumer WTP. There is also a lack of alternative financial mechanisms and scientific information on natural resource valuations to support the institution of PAs in Laos.

In response to these gaps, this paper offers scientific evidence on these issues that will help decision-makers and park managers estimate users’ WTP for the conservation of PAs and determine which factors influence users’ WTP. Ultimately, this study sought to assess the aggregate WTP for the conservation of the PCV PA by examining related household perceptions and factors influencing household WTP for the ecological protection of the PCV PA.

2. Materials and Methods

2.1. Introduction of the PCV PA

The PCV PA was established in 2005 with a total area of 223.05 km² ranging from 18°23′–18°25′ N and 105°02′–105°03′ E, at elevations of 1000–1820 m above sea level [5]. It is located in Bolikhamxay Province, Lao PDR. Approximately 17,300 people live around the protected area. The area is located on the steep western slopes of the Annamite Mountains, which form a natural border between Laos and Vietnam (specifically, Heng An and Hating). The eastern side is linked to Phu Mat National Park in Vietnam [24] (Figure 1).

The management authority of this PA is the Provincial Agriculture and Forestry Office of Bolikhamxay Province, Ministry of Agriculture and Forestry.

A cloudy, humid, evergreen forest comprises the main watershed area that provides a natural water source and a significant sanctuary for wildlife [25]. Therefore, the PCV PA is rich in endemic wildlife species unique to its tropical climate and abundant resources that could provide ecosystem services to benefit social and national economic development. The PCV PA is a diverse hotspot home to high montane evergreen forests; notably, evergreen forests, mixed deciduous forests, and other kinds of forests cover 74.12%, 22.80%, and 30.07% of the area, respectively (Figure 2). The area is exposed to varying climatic conditions from east to west. While the area’s peculiar ecosystem has historically been home to many endemic species, scientific data on its flora remain insufficient [26].

The ecotourism activities provided in this protected area include hiking, waterfall bathing, camping, and wildlife viewing. Therefore, a lack of proper management of human activities would cause resources to deteriorate in the near future. Figure 3 shows the number of local visitors to the PCV PA from 2017 to 2020.

Habitat loss has occurred in lands inside and outside protected areas for several reasons, including degradation that started in the late 1980s, when economically desirable resin used in scent was overharvested [27].

The majority of the population in the PCV PA are subsistence rice farmers, subsistence hunters, and gatherers who eat small animals, such as squirrels and birds. However, local subsistence practices are not the main reason for the decline in biodiversity. Instead, poachers who hunt rare and endangered animals are typically involved in removing endangered species, because of their scarcity and intended use in localized foods and medicines in neighboring countries [28].

Furthermore, many hunters and trappers are from neighboring countries, complicating law enforcement. Of the significant critical threats found in the PCV PA, 49% were wire snares, followed by poacher camps, logging, and people with guns at 17%, 14%, and 8%, respectively [13].

2.2. Economic Valuation Method

The economic valuation of the use and non-use of ecosystem services, such as public goods, is highly controversial. However, indirect market-based techniques can often provide helpful estimations about respondent preferences in response to changes in environmental status [29]. There are two economic valuation methods to elicit respondent preferences: revealed and stated preferences. Of these two methods, contingent valuation, a stated preference method, was used in this study.

The revealed preference approaches are practical when dealing with the use of the value of ecosystem services, such as recreation, which are commonly assessed using travel cost and hedonic price methods. However, revealed preferences cannot capture non-use values, and the existence value of the ecosystem remains overlooked [30]. The CVM needs individuals to state their preferences for some environmental goods and services or change in resource status, by answering questions about hypothetical choices. The respondents to a CVM questionnaire will be asked a type of question about how much they would be willing to pay to ensure a welfare gain from a change in the provision of a non-market environmental commodity [31].

As discussed above, the CVM is a stated preference method that involves consumer surveys to elicit individuals’ hypothetical WTP for non-market goods, such as biodiversity, ecotourism resources, and PAs, as well as their willingness to accept compensation for the loss of those benefits [32]. Income constraint is a limitation: poor individuals are less inclined to pay. As a result, this study’s findings are influenced by average income levels. However, CVM has the advantage of being able to assess difficult-to-measure non-user values or the worth of non-traded commodities and services [33].

The CVM survey consists of three sections. The first section is characterized by the environmental change described by the policy formulation and the description of the contingent market. The second section addresses the respondents being asked to state their monetary valuation of the related policy formulation, and the third section is a set of questions that collect sociodemographic information about the respondents [29].

M. Abdullah et al. [34] evaluated the willingness of residents (non-visitors) to pay for the conservation of a national heritage site in Malaysia. They adopted the Contingent Valuation Method (CVM) to elicit the 410 respondents from urban and rural households. The study’s findings revealed that the mean WTP ranges (from RM 53.24 to RM 67.22), which could contribute to annual revenue, ranged from RM 66.3 million to RM 83.8 million in total, and monthly household income was a positive and significant predictor.

In Thailand, Rakthai [35] measured households’ willingness to pay (WTP) for biodiversity conservation in the Lower Mekong River Basin and looked at the factors influencing WTP. They used the single-bounded closed-ended contingent valuation method questions to poll 763 households randomly and then analyzed the data using non-parametric and logistic regression models. The households’ age and education level were the two most important factors impacting their WTP. The overall annual economic value was estimated to be at USD 153,471.38.

The other CVM has also been applied in various research areas, such as forest conservation in Sweden [33], marine protected areas in Southeast Asia [36], non-point source pollution control [37], water ecosystem services [38], and valuing cultivated land protection [39].

2.3. Contingent Valuation Survey

This research sample size is calculated using Taro Yamane’s formula, with a 95% confidence level. Many previous studies have used this equation for their sample size determination [40]. According to the data from the Bolikhamxay Provincial Statistic in 2019, there are about 3441 households. The calculation formula of Taro Yamane [41] is presented in Equation (1), as follows.

$$𝓃=\frac{\mathsf{{\rm N}}}{1+\mathsf{{\rm N}}{\left(e\right)}^2}$$

(1)

• where:
• $𝓃$ = denotes the sample size;
• N = denotes the population under study;
• $e$ = denotes the margin error (0.05).

Substitute numbers in formula:

$$𝓃=\frac{3441}{1+3441{\left(0.05\right)}^2 }$$

$$𝓃=358\left(Rounded\right)$$

This study collected primary data through a household survey to estimate resident WTP for restoring and conserving PA biodiversity. A pilot survey was conducted with 25 respondents to finalize the contingent valuation questionnaire. The survey was conducted over two months, September and October 2021. The sampling method used the questionnaires survey, face-to-face interviews, and random sampling techniques; a sample size of 265 was chosen, and 263 respondents were validated for use in the analysis.

The respondents first received an explanation of the study’s purpose. They were informed that their information would be kept confidential and used only for academic purposes to obtain straightforward responses.

A structured questionnaire with a hypothetical question was designed for the survey, in line with standard contingent valuation techniques. The survey was conducted through in-person interviews. The questionnaire was divided into two parts. The first part of the questionnaire explored the socioeconomic characteristics of the respondents, such as gender, age, ethnic group, household size, education, occupation, annual household income, distance from their house to the protected area, and the number of times they visited the PA. The second part of the questionnaire was designed to elicit respondent WTP for the conservation of the PCV PA, by providing a hypothetical scenario (Figure 4). The respondents first explained the current status and challenges confronted by the PCV PA. The interviewer then informed them that they could take action to participate in the conservation of the PCV PA. Follow-up questions were used to explore the reasons for their WTP. In addition, respondents were asked about a change in their current WTP compared to that before COVID-19 to provide a reason behind the changes, and to state their changed amounts.

Two scenarios in Figure 4 were one of the questions that included WTP payment card format questions. The respondents were asked about their responses, “Yes” or “No”. If they said “No” that means they selected scenario 1; i if they said “Yes”, they were asked the reasons why and how much they were willing to pay, by choosing the given distribution amount from payment card format questions.

2.4. Econometric Model and WTP Estimation

The logit model is the preferred econometric method frequently employed to analyze data obtained from contingent valuation surveys. This study employed binary logistic regression (Equation (2)) to determine respondent WTP for biodiversity conservation, because the data had only one dependent variable with a binary choice [39].

Equation (3) was adapted from the logit model from Equation (1) used in this study to calculate the mean willingness of respondents (Equation (4)) and the aggregate WTP (Equation (5)).

$$\mathrm{Y}={\beta }_0 +{\sum }_{\mathrm{j}=\mathrm{i}}^{\mathrm{k}}{\beta }_j{\mathsf{{\rm X}}}_{\mathrm{ij}}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(2)

where Y is the dependent variable, which measures the WTP of respondents for conservation of the PCV PA, Y takes the dummy variable of 0, if the respondent’s WTP is less than or equal to zero, and otherwise it takes 1.

$$Y=\left\{\begin{array}{c}1 if\\ 0 if\end{array}\right.\left.\begin{array}{c}Y>0\\ Y \le 0\end{array}\right\}$$

• ${\beta }_0$= constant or intercept;
• ${\beta }_j$= coefficient of explanatory variables;
• ${\mathsf{{\rm X}}}_{ij}$ = explanatory variables;
• ${\epsilon }_{i }$= error term.

Therefore, the final equation used in this research is:

$$\mathrm{Y}\left(\mathrm{WPT}\right)={\beta }_0+{\beta }_1\mathrm{GEN}+{\beta }_2\mathrm{AGE}+{\beta }_3\mathrm{HS}+{\beta }_4\mathrm{EDU}+{\beta }_5\mathrm{AHI}+{\beta }_6\mathrm{DPA}+{\beta }_7\mathrm{NoVP}+{\beta }_8\mathrm{ATT}+{\mathsf{\epsilon }}_{\mathrm{i}}$$

(3)

$$\mathrm{Mean}\mathrm{WTP}=\frac{{\mathrm{WTP}}_{\mathrm{total}}}{\mathrm{N}}$$

(4)

where Mean WTP is the average respondent WTP, ${\mathrm{WTP}}_{\mathrm{total}}$ is the total amount of willing respondents, and N is the total number of willing respondents.

The total economic value for conservation of the PCV PA was calculated as

Aggregate WTP = MWTP × THH

(5)

where MWTP represents the mean willingness to pay per household per year, and THH represents that total number of households in the study site.

The logit model in Equation (1) was performed using the maximum likelihood estimation method, the most common technique for calculating the logit model [42]. Statistical analysis and estimating factors of the logit model were performed using STATA (ver.11.0). The variables used in Equation (2) are provided in

Table 1. Variables in the logit model.

Variable’s Code	Variable’s Name	Variable’s Short Meaning	Unit
Dependent Variable
WTP	Willingness to pay	Respondents’ willingness to pay for the conservation of the PCV PA	LAK
Independent Variables
GEN	Gender	Respondent gender (dummy variable: 1 = male and 0 = female)	Dummy variable
AGE	Age	Respondent age category	Number of years
HS	Household size	Number of persons in the household	Number of persons
EDU	Educational level	Respondent’s level of education	Number of studied years
DPA	Distance from home to the PA	The distance from the respondent’s residence to the PA	Kilometers
NoVP	Number of visits to the PA	Number of times the respondent visits the PA	Number of times
AHI	Annual household income	Respondent’s average yearly income	LAK
ATT	Attitude towards PA conservation	Respondents’ attitude towards conservation for future generation (dummy variable: 1 = yes and 0 = no)	Dummy variable

.

2.5. Variables Hypothesis

Factor analysis was used to determine the nature of the underlying latent variable represented by each factor, by identifying the factors that offered the maximum loading variables for each factor.

GEN of the family head significantly influences household WTP and decisions because men and women have different characteristics. In this study, the family head, who is a man, is believed to be able to pay more than women. Therefore, the sign of the correlation between willingness to pay and the gender of the household head may be positive or negative [42,43,44,45].

AGE is expected to partially affect the willingness to pay for conservation. As a requirement of physical strength, a young family head is likely to benefit from natural resources more than a family head with a higher age. In addition, the family head’s age reflects his working experience, which affects his economic return. Thus, there are possible positive and negative relationships between age and the dependent variable [45,46,47,48].

HS is an independent variable, measured as the number of people living together in a house. The number of persons in the household is one of the determinant factors correlated to WTP for natural environmental preservation. Households with many people, who can work an active job to secure their household income, would be more likely to contribute funds for ecological maintenance than households with fewer people [18,42,49,50,51].

EDU attainment is categorized into seven groups: no formal education, primary school, secondary school, upper secondary school, technical school, bachelor’s degree, Ph.D., and others. It is calculated by the number of years the respondent spent in school. Respondents who are more educated are more likely to support the program, because they are more aware of the benefits of a better environment (forest). Furthermore, educated people are better aware of the negative consequences of natural resource degradation and the benefits of employing sustainable forest services; therefore, they are more worried about these resources. In addition, higher educational attainment is viewed favorably by WTP, because it reflects a greater ability to pay. Therefore, the predicted sign of the variable coefficient has a positive influence on WTP [47,52,53,54,55].

DPA is a continuous variable measuring the distance in kilometers from the respondent’s home to the protected area. This study expects that the respondent’s distance from the PA is negatively related to the WTP for biodiversity conservation. Due to the distance between residents’ permanent houses, the PA might be impacted during times of travel, and they may not gain the benefits of forest products [18,51,56,57].

NoVP is a continuous variable that refers to the respondents’ number of visits or the use of ecosystem services in the PA. It is also a significant factor affecting respondent WTP; people who visit the protected site more frequently may be more willing to support it because it will obviously benefit them [48,52,58,59,60].

AHI is a continuous variable for aggregate annual household income, measured in LAK. This variable refers to the respondent’s ability or inability to pay. Economic theory suggests a positive association between demand for goods or services and income, taking forest goods and services as common goods. This indicates a positive relationship between income and demand for environmental quality improvement. Thus, it is expected to have a positive impact on high-income respondents [46,53,55,56,61,62,63].

ATT refers to respondent awareness of contributing funds to environmental improvement. This significantly positively influences respondent WTP. On the other hand, attitudes toward pricing policy have also been investigated as a significant factor in WTP. Therefore, it is vital to consider this predictor before implementing any policy-pricing system. In addition, this would help determine which visitors might generate more revenue for forest resource conservation [39,42,45,53,55,64].

3. Results and Discussion

3.1. Sociodemographic Profile of Respondents

Descriptive analysis was used to analyze the respondents’ demographics and characteristics. All information was defined as percentages and frequencies.

Table 2. Respondent profiles.

Characteristics	Results
	Frequency	Percent (%)
Gender
Male	287	79.06
Female	76	20.94
Age
20–29	10	2.75
30–39	130	35.81
40–49	119	32.78
50–59	61	16.80
60–69	36	9.92
>69	7	1.93
Ethnic
Lao Loum	308	84.85
Mhong	33	9.09
Lao Theung	22	6.06
Education
Primary school	26	7.16
Secondary school	90	24.79
Upper secondary school	152	41.87
Technical school	85	23.42
Bachelor’s degree	9	2.48
No education	1	0.28
Occupation
Farmer	233	64.19
Upland agriculture	88	24.24
Government employee	28	7.71
Retailer	9	2.48
Businessperson	5	1.38
Distance to PA
2–5 kms	193	53.17
6–9 kms	84	23.14
10–15 kms	86	23.69
Household annual income (LAK) *
1,000,000–4,999,999	82	22.59
5,000,000–9,999,999	187	51.52
10,000,000–14,999,999	51	14.05
15,000,000–19,999,999	25	6.89
20,000,000–24,999,999	12	3.31
25,000,000–29,999,999	4	1.10
30,000,000 and above	2	0.55
Household size
2–5 members	201	55.37
6–9 members	136	37.47
10 members and above	26	7.16
No. of visits to PA
4–8 times	83	22.87
9–12 times	50	13.77
12–15 times	96	26.45
More than 15 times	134	36.91
Is the PCV PA important to you
Important	345	95.04
Not important	18	4.96

* Note: exchange rate at the time of the study was USD 1 = LAK 11,458.

presents a summary of the respondents’ demographic information. The gender of the respondents indicates that men constituted 79.06% of the interviewed sample, while women represented 20.94%. The mean age of respondents was 45 years. The majority (68.60%) of respondents were between 30 and 49 years of age. About 84.85% of the respondents were part of the Lao Loum ethnic group. The average number of family members in each household was six. Approximately half of the respondents (55.37%) had two–five family members per household. From the interviews, 41.87% of the respondents had completed upper secondary school. Meanwhile, 7.16%, 24.79%, 23.42%, and 2.48% had completed secondary school, technical school, and bachelor’s degrees, respectively; only one respondent (0.28%) had no education. In terms of occupation, the respondents were mainly farmers (64.19%). More than half of the respondents (53.17%) lived close to PAs. The average annual household income of the respondents was LAK 8,601,818 per year. Almost all respondents perceived the PCV PA as important.

3.2. WTP Estimation Results

In the formal survey, 25.34% (92) of respondents refused to pay, and 74.66% (271) were willing to pay (WTP > 0) for the preservation of the PCV PA.

The mean of the positive respondents’ WTP was LAK 27,055 or USD 2.36. The minimum was LAK 6000 or USD 0.52, and the maximum was LAK 120,000 or USD 10.47 per household.

The aggregate WTP was calculated as the average positive WTP multiplied by the total number of households in the study area (3441). Therefore, the total economic value for PCV PA was approximately LAK 93,097,461 or USD 8125 per year.

Respondents who agreed to pay were then asked to state the reasons for their WTP. Among the 271 respondents who were willing to pay, 25.46% stated that they chose to conserve the PCV PA because of its natural value and attractiveness for tourism. Meanwhile, 65 respondents (23.99%) stated that they wanted to conserve it for future generations, 45 (16.61%) stated that they were concerned about the people around the area who relied on it for food and services, 40 (14.76%) wanted to contribute because of the area’s other usable values, 39 (14.39%) wanted to contribute for its future uses, and 13 (4.80%) wanted to contribute to protect endangered plants and animals.

Respondents who refused to pay for the conservation of the PCV PA were asked about their reasons. The follow-up questions asked respondents to choose one reason from the following options—I do not want to pay additional for my monthly expense; I have a low income and not enough money to pay, and I must pay for others’ necessities; I think the conservation of the PCV PA is not so important; I do not think my payment will not use for conserving purposes for the PCV PA; I do not trust agencies that manage conservation fund; people who earn a higher income should pay; the Lao government should pay and respond; and other reasons. Their answers are provided in

Table 3. Descriptive statistics of socioeconomic variables.

Variable	Positive WTP		Negative WTP		Total (N = 263)
	Mean	Std. Error	Mean	Std. Error	t-Value	Mean
GEN	0.78	0.02	0.80	0.04	0.33	0.78
AGE	45.84	0.64	40.53	1.04	2.51	44.50
HS	5.73	0.12	5.63	0.22	−1.95	5.70
EDU	11.12	0.16	9.40	0.26	2.41	10.68
DPA	6.35	0.21	6.51	0.36	−0.08	6.39
NoVP	12.48	0.22	11.51	0.40	0.79	12.23
AHI	9,708,708.48	339,362.18	5,341,304.34	217,136.50	5.08	8,601,818.18
ATT	0.23	0.02	0.01	0.01	2.89	0.23

. Among the 52 respondents who refused to pay, 56.52% stated that they did not want to increase their monthly expenses, 23 (25%) stated that their incomes were already too low to contribute, 10 (10.87%) did not think their payments would be used for conservation of the PCV PA, 5 (5.43%) advised that the PCV PA was too far from their residence to want to contribute (they did not want to visit), and 2 (2.17%) did not trust the agencies that managed the conservation fund.

The socioeconomic variables of sampled households used to analyze WTP behavior are provided in Table 3.

3.3. Factor Analysis

Eighty explanatory variables, including GEN, AGE, HS, EDU, AHI, DPA, NOVP, and ATT, were used to estimate the WTP probability. Of these, four variables, AGE, EDU, AHI, and ATT, significantly influenced WTP. In contrast, GEN, AGE, EDU, AHI, DPA, NOVP, and ATT were positively correlated with household WTP. Only HS and DPA were found to have a negative association with household WTP. These results may be explained as follows.

Age of respondent (AGE) was a continuous variable representing respondent age in years. This was found to positively affect the coefficients of the model (

Table 4. Estimation results of binary logit model.

Variable	Code	Coefficient	Marginal Effect	P > Z
Gender	GEN	0.167 (0.357)	0.017 (0.038)	0.639
Age	AGE	0.041 (0.015)	0.004 (0.001)	0.012 **
Household size	HS	−0.158 (0.079)	−0.016 (0.008)	0.051
Education level	EDU	0.146 (0.058)	0.015 (0.006)	0.018 **
Annual household income	AHI	2.685 (0.466)	0.286 (0.055)	0.000 ***
Distance from home to PA	DPA	−0.006 (0.044)	−0.000 (0.004)	0.875
No. of visits to PA	NoVP	0.030 (0.043)	0.003 (0.004)	0.495
Attitude towards PA conservation	ATT	2.960 (1.033)	0.316 (0.085)	0.000 ***
Intercept		−43.9886 (7.1804)
No. of observations		363
Likelihood ratio		x2 = 125.04. p < 0.05
Log-likelihood function		−142.97195
McFadden pseudo R2		0.3042
Goodness of model		x2 = 9.453. p > 0.05
Percentage of correct prediction		80.99

Note: *** and ** represents significance level at 1% and 5%, respectively. Numbers in parenthesis are standard errors.

). The coefficient weight in the model was 0.041, with statistical significance at the 5% confidence level. The positive and significant correlation between age and WTP may be due to two reasons. First, older people are wise and consider the bequest value of forest ecosystem services; therefore, they are eager to transfer resources to their children or grandchildren. Therefore, the probability of WTP for conservation activities can increase. Another probable reason is that older people are more financially stable, so they may be more willing to contribute to maintaining PAs. This finding is in agreement with the results of previous studies [18,35,52,63,65].

The marginal effect estimates (Table 4) also show that keeping the effect of other factors constant, an additional one-year increase in the age of the household head/representative led to the probability of an increase in WTP by 0.4% for conservation; this was significant at the 5% level.

The household head’s education level (EDU) was found to have positive coefficients in the models (Table 4). The results indicated that the education of household heads increased the WTP for forest conservation practices to enhance the benefits of the forest. The marginal effect result shows that for each additional increment in years of education, the probability of WTP for forest conservation practices increases by nearly 1.5%. This may be because educated respondents were concerned about the natural environment; education raises environmental awareness and values natural resources. The positive correlation between education level and a household’s WTP for conservation in this study is consistent with many previous studies [46,48,58,59].

Respondents’ annual household income (AHI) was another critical variable with a positive coefficient of 2.685 weights in the logit model (Table 4). This result was statistically significant at the 1% level. Households with high incomes tend to have a higher WTP for the committed system than their counterparts with low incomes. The marginal effect estimates for the household annual income variable indicated that a one LAK increase in a household’s income could increase the probability of WTP for forest maintenance by 28.6%. This finding is consistent with those of many previous studies [42,47,53,65,66,67].

Attitude towards PA conservation (ATT) was a dummy variable that was found to positively affect respondent WTP, with a coefficient of 2.960 weights in the logit model (Table 4). Furthermore, it is statistically significant at the 1% level. The marginal effect estimates for household attitudes toward protected area conservation revealed that approximately 31.6% of respondents agreed that conserving the PCV PA is essential for present and future generations and tourism. The positive relationship between the attitude toward environmental protection and the willingness of residents to pay for conservation in this study is in line with the findings of previous studies [39,53,55,61,64].

3.4. Examining Changes in WTP before and after COVID-19

Respondents were asked about their maximum WTP before and after COVID-19. The mean respondent WTPs before and after COVID-19 were LAK 3138.66 and LAK 1899.16, respectively.

To investigate changing respondent preferences in WTP before and after COVID-19, this study also asked respondents if there were any changes in their preferences before and after COVID-19. From the interviews, the study found that more than 60% of respondents answered that COVID-19 impacted their WTP, while the rest expressed that the COVID-19 situation did not really affect their WTP. The main reason behind changes in WTP during the COVID-19 pandemic was related to changes in incomes.

Table 5. Correlation in respondents’ WTP before and after COVID-19.

		Before	After
WTP before COVID-19	Pearson correlation	1	0.2815
	P (T <= t) two-tail		0.000
	N	119	119
WTP after COVID-19	Pearson correlation	0.2815	1
	P (T <= t) two-tail	0.000
	N	119	119

shows a significant positive relationship between WTP before COVID-19 and WTP during COVID-19, at the 1% significance level. This means that respondents who were willing to pay before COVID-19 were still willing to pay after COVID-19.

The difference between respondent WTP before and after COVID-19 was LAK 1231.092, as shown in

Table 6. Difference in mean WTP of respondents before and after COVID-19.

	Mean	Std. Error.	t Stat	p-Value	df	Lower 95%	Upper 95%
WTP before and after COVID-19	1231.092	232.474	5.53	0.000	118	824.428	1745.234

; it was statistically significant. The null hypothesis of equal WTP means was rejected, as t (118) = 0 and the t-test = 6.14.

3.5. Discussion

This research investigated local visitors’ WTP for the PCV PA and the factors affecting their WTP. Based on these results, an appropriate conservation fund was suggested for the maintenance of the PCV PA.

Ultimately, this research indicates a high potential for positive WTP (74.66%) to recover and conserve the PCV PA. Specifically, the sociodemographic information of the respondents revealed that the average visitation frequency was 5.70. Almost 95.05% of respondents stated that it is vital to conserve the PCV PA’s natural value for future generations and tourists, though other notable reasons for contributing including natural eco-values and sustainable use. Willing contributors are likely wealthy and aware of biodiversity, habitat destruction, and the high threats of illegal poaching and trading [13]. Therefore, maintaining a biological system through conservation donations can be a payment vehicle for conserving the PCV PA.

Additionally, there are 13 villages around the PCV PA, and residents rely heavily on the services provided by the PCV PA. Hence, promoting local community livelihoods could potentially mitigate the conflict between the community and the area’s protection and robust conservation activities in the PCV PA. The main reason respondents stated a negative WTP was that they did not want to increase their monthly expenses. This is a reason for negative WTP in other studies [42,55,65].

When investigating the determinants impacting respondent WTP, age, education level, annual household income, and attitude toward PA conservation proved to be significant independent variables for WTP. These explanatory variables positively correlated with WTP; for instance, respondents with a higher annual income were more likely to pay for conservation than those with lower incomes. A study in the Wolf-Washa Forest area in Iran [18] found that the yearly household income had a significant positive impact on household WTP (p < 0.05), which agrees with the results of this study. Regarding the positive relationship between age and WTP, it is notable that older people tend to be wise and, thus, may be more likely to have considered the bequest value of forest ecosystem services; this may make them more eager to transfer resources to their children or grandchildren. This finding agreed with the results for the Karagol Natural Park of Ankara in Turkey, in which the age of respondents categorized between 31 and 45 years was found to increase the probability of WTP [62]. Respondent education level also had a positive relationship with WTP: household heads with higher levels of education were more willing to pay for forest conservation practices to enhance the benefits of the forest. This result was in line with the findings of a study of Kubah National Park in Malaysia, which found the education variable to be significant at the 1% level [47].

Attitude toward PA conservation was one explanatory variable that proved statistically significant at the 1% confidence level. This finding revealed that respondents were expected to agree that the preservation of the PCV PA is essential for present and future generations and tourism. This finding is in line with an estimation for the Kanas Nature Reserve in China, presented by Han et al. [55]. Research conducted by Aseres and Sira [53] on PAs in Ethiopia found that attitude toward conservation had a positive effect on WTP, which aligns with the results of this study. Meanwhile, Witt [45] explored visitor WTP for a PA in Mexico and found a positive correlation between attitudes toward the conservation of Pas and WTP, which also aligns with the results of this research. While this study found a negative effect coefficient between household size and WTP, it did not significantly impact WTP. Additionally, this study revealed a positive coefficient between gender (male) and WTP, which did not significantly influence WTP.

In the payment format question, the mean WTP was LAK 27,055 or USD 2.36 per household per year (approximately 0.31% of annual household income). The aggregate WTP was LAK 93,097,461 or USD 8125 per year—notably, this potential revenue is approximately USD 3125 higher than the current government budget allocated for each PA for conservation. However, compared to many previous studies, this estimation of aggregate WTP is relatively low. This may be due to several reasons; for example, this study’s respondent group had a lower annual income than the average national income and the average incomes used in other research. The average annual income in this study of respondents who were willing to pay for conservation was USD 847.3; this is similar to the average income of the respondents in Bakar [47] (between RM 2001 and RM 4000 or approximately between USD 480 to USD 960), which was also lower than in other studies, in which incomes were, for instance, BR 34,962 or USD 1048.8 [18], USD 2000 to USD 5000 [51], USD 17,699 [35], and Rs 40,453 or USD 525.88 per month or USD 6310 per year [68].

Ultimately, compared to previous other aggregate calculations, the figure from this study was relatively low. Amirnejad et al. [42] found that Iranians were willing to pay USD 30.12 to protect the northern forests of Iran, with an aggregate WTP of about USD 376.5 million; Sadikin et al. [50] discovered that domestic tourists had a WTP for ecotourism in the National Park of Indonesia of about USD 3 per person per year, for an expected total economic value of USD 65,512 per year; Sherif’s [69] analysis of 212 household heads using CVM on WTP to conserve national parks in Ethiopia found that the average WTP was around USD 2.96 per household/year, for a predicted annual revenue of USD 11,860; Kragt et al. [27] estimated that the mean WTP of local people in China for ecosystem services was about USD 24.48, with an aggregate WTP of USD 52.72 million; and Bhat and Sofi [65] reported a household WTP for biodiversity conservation in India of USD 3.32, for an annual total of roughly USD 3,915,845.

Compared to the average global budget for protected areas, the global mean budget for protected areas is USD 893 per km². Meanwhile, the mean for developed countries is USD 2058 per km², whereas the mean for developing countries is USD 157 per km² [70]. This study uncovered a budget of about USD 36.42 per km², which is a lower-than-average global fund for conservation in the developing world.

Notably, the WTP in the study changed with COVID-19. The mean respondent WTP before COVID-19 was LAK 3138.66, while the mean WTP after COVID-19 was LAK 1899.16—a LAK 1231.092 difference. The main reason behind this change during the COVID-19 pandemic was changes in income.

4. Conclusions

This study aimed to assess the total economic value of the PCV PA through local residents’ WTP and identify the determining factors of their WTP. The study used the CVM and a logit model to estimate the WTP of 363 local households around the PCV PA. The findings revealed that most local people (75%) were willing to pay to maintain the PA despite various pressing problems. Most respondents were willing to pay for conservation because they wanted to protect the area for future regenerations, had natural eco-values, and wanted to support ecotourism. The mean WTP was LAK 27,055 or USD 2.36 per household per year, and the total economic value was LAK 93,097,461 or USD 8125 per year. The probability of respondents’ WTP for the PA conservation was significantly affected by age, education level, annual household income, and attitude toward PA conservation. The PCV PA is used by local residents as a source of water, wood for energy and housing, agricultural practices and tools, meat (from wild animals), honey, and recreation. It is also a site of protection from topsoil erosion and heavy rainstorms and home to watersheds.

The study results also contribute insights into using WTP in sustainable funding to develop market-based conservation strategies in protected regions to minimize ongoing biodiversity losses and preserve natural resources. The study also provides a fundamental analysis of the impact of several predictors, which could help in modeling user WTP. Therefore, estimating user WTP for the suggested alternative financial mechanism can help conservation officers, policymakers, and PA management consider alternative financing mechanisms beyond government conservation funds that generate adequate conservation finance and foster strong relationships with residents.

The PA in this study has been degraded by illegal wildlife hunting, overharvesting of non-timber forest products, shifting cultivation, bush fires, illegal logging, and other disturbances. Therefore, immediate action is required. In order to maintain and secure its services, protection and conservation approaches should be implemented, but this requires financial support. The present research suggests that if the government or other concerned sectors work with local communities, financial constraints can be solved.

4.1. Policy Recommendations

This study adds to the current non-market valuation literature by assessing visitor WTP using CVM to strengthen the management and protection of PAs, mainly where conservation funding is insufficient. The research offers important insights into how WTP might be used to build market-based conservation measures in PAs, mitigate ongoing biodiversity losses, and preserve natural resources.

Most respondents were willing to pay to conserve the PA because they believed it was vital for present and future generations. However, this study also found that resident WTP was highly related to resident educational level and income. This suggests that increasing residents’ educational and economic opportunities can help them to become more environmentally conscious and, thus, more willing to support PAs. Therefore, establishing education and awareness-raising programs related to conservation and restoration activities may help stabilize the use of natural resources for the PCV PA and its ecosystem services. The government and concerned organizations, such as local PA administrations, can find guidelines for environmental education programs for local residents on natural environment conservation topics and related activities. In addition, local authorities should consider creating a co-management plan or program to encourage strong participation in conservation activities. Additionally, alternative income-generation programs may enable people who are currently low-income earners to financially support conservation in the future.

Ultimately, the current payment vehicle system for PAs in Laos is not fully effective. While the above-listed strategies may encourage donations, a payment channel could also be established through electricity bills, which are seen as the most efficient way to collect money, as electricity is provided in all districts in Laos. However, management should also seek donations to implement conservation activities, rather than depending on externalities and the government budget. To encourage residents to support the sustainable development and maintenance of protected areas, the government or PAs organizations should revise the conservation funding policy, by using the mean WTP of USD 2.36 multiplied by each targeted household of the PA in Laos. Community involvement should be ensured in the decision-making, planning, and formulation of strategies related to site conservation.

4.2. Limitations and Implications of the Study

The main limitation of this study is that it only assessed the WTP of local residents. Thus, future studies could examine the WTP of foreign tourists visiting the PCV PA to obtain every user’s WTP for the PCV PA, determine the difference in WTP between foreign and local visitors, and analyze the costs and benefits of the PCV PA. Investigating these aspects in future research on the PCV PA would be valuable.

Meanwhile, donations were the payment mechanism used in this study. Therefore, another study with a different payment mechanism, such as an entry fee or tax, should be conducted to perceive the effects of the payment mechanism in valuation studies and compare WTP among various payment mechanisms.

There is also a need for further contingent valuation studies of conservation finance policies in larger-scale PA throughout Laos. Numerous earlier studies have been conducted in locations across the globe, where high-end community-based conservation is the main driving parameter, with various areas and visitor characteristics. More extensive and organized cross-national investigations are required to better understand visitors. The fund pricing strategy may be influenced by analyzing the particular site or national characteristics, such as visitation rates, management structures, and cultural amenities derived from customers’ WTP.