1. Introduction
RESs are included in the Common International Classification of Ecosystem Services (CICES), which is identified as an important category of cultural ecosystem services (CES) [
1]; these contribute to human society through leisure and entertainment opportunities and experiences [
2,
3]. The first time that “recreation” and “ecosystem” were combined was in the process of ecosystem service value accounting, conducted by Costanza et al. in 1997 [
4]. In 2005, the Millennium Ecosystem Assessment (MA) officially classified recreational ecosystem services as “recreational ecosystem services”. RES is defined as “the recreational pleasure that people derive from natural or artificial ecosystems” [
5].
Quantifying recreational ecosystem services (RESs) is essential due to the valuable visual and environmental experiences offered by recreational landscapes (RLs) [
6,
7]. Unlike other ecosystem services like food and water supplies, analyzing RESs presents significant challenges owing to the lack of appropriate frameworks and information on recreational resources [
6,
8]. Traditional data collection methods, such as questionnaires and preference interviews, are often expensive, time-consuming, subjective, and do not display spatial distribution information effectively [
9]. Geotagging photos obtained from social networks, although promising, remains an unpopular approach in many countries [
10,
11]. Increasingly, studies are turning to the maximum entropy (MaxENT) model or its derivatives, such as the Social Values for Ecosystem Services (SolVESs) model, which combine landscape resource points with environmental data to map the supply of CESs or RESs; this is considered to be an effective approach [
12,
13,
14,
15,
16]. The MaxENT model, which is commonly used to predict species niches and distributions, offers a means to assess RESs by exploring the relationship between environmental variables and the occurrence location of RL points [
17,
18]. This method enables the description of occurrence space and quality levels of different RL types, providing a relatively objective map of RESs supply and quantifying the impact of each environmental variable [
18,
19]. However, accurately rendering RES supply requires addressing challenges associated with depicting various RL types uniformly, as this approach often overlooks their differences, impacting the accuracy of RES supply rendering. The supply of RLs is diverse, including grassland [
20], farmland [
15], wilderness [
21], and other types. Most existing studies focus on a single RL. When depicting RLs, many types of landscape are often drawn in a unified manner without considering the differences among RL types, thus affecting the accuracy of RES supply rendering. Therefore, further optimization is needed to achieve more precise mapping of RESs and accurately map the supply of recreation ecosystem services.
The ecosystem services framework seeks to capture nature’s benefits to society and human wellbeing by assessing the monetary and non-monetary values of ecosystem functions [
22,
23]. Research on RESs has gained prominence, with a predominant focus on non-monetary evaluations [
12,
24,
25]. However, quantifying the monetary value of RESs remains challenging, primarily due to the absence of market alternatives that can approximate the prices associated with these non-exclusive goods [
26]. Various methods, such as the travel cost method (TCM), leverage social media data to estimate travel distances and capture broad public preferences across multiple locations [
27]. While classical economic behavior survey methods like interview datasets and on-site interviews offer broad insights, they are often time-consuming and costly and are not commonly used in RES value assessments [
28,
29]. Another approach, meta-analysis, attempts to estimate the monetary value of RESs by considering sociocultural factors and their geographical contexts to distinguish user groups in preference assessments at different spatial scales [
30]. However, for large-scale areas like the Qinghai–Tibet Plateau or less-developed regions with significant recreational value, these methods still have limitations.
RLs are the parts that make up RESs, and their visual aesthetic qualities are clearly considered to be an important natural resource, like water, soil, mines, and fossil fuels [
31]; they are beneficial to human beings’ physical and mental health [
32,
33]. The higher the quality of the visual aesthetics, the greater the chance a site has of attracting recreational visitors, thus increasing the tourism potential of a place [
34]. The consistency of aesthetic preference judgment is affected by many factors, such as landscape quality, landscape type, and the mental image attained among recreational visitors [
35,
36]. At the same time, RL patterns play a key role in the play of the service value of RESs [
37]; different landscape patterns will have an impact on the recreational experience of recreational users [
38,
39,
40], and it can provide reference for decision makers in reasonably, sustainably planning for and developing potential recreation areas [
41]. The Qinghai–Tibet Plateau is a world-class leisure tourism attraction, with high-quality natural resources and a unique cultural landscape [
42,
43,
44]. The Qinghai–Tibet Plateau National Park Group (QTPNPG) is the potential area linkage body of the Qinghai–Tibet Plateau National Park construction. The exploitation and realization of the RES of the QTPNPG is very important for the sustainable development of the Qinghai–Tibet Plateau, the construction of a beautiful China, and the achievement of the Sustainable Development Goals, as set by the United Nations [
45,
46]. The objectives of this study are as follows: (1) To map the spatial distribution of the RES of the QTPNPG from the perspective of RLs supply, and to determine the recreational potential area of the QTPNPG. (2) The alternative value approach was used to monetize the value of the RES in the QTPNPG and compare the monetary value differences between national parks. (3) Evaluate the RLs of the QTPNPG, analyze the spatial pattern and potential impact of the RLs in various national parks, and provide suggestions for sustainable recreation site development.
3. Results
3.1. Verification of Recreational Landscape Type Identification Accuracy
The jackknife procedure was used to analyze the relative influence degree of environmental variables on the potential distribution of RLs, and the receiver operating characteristics curve was used. AUC was used to verify the model simulation results. The fit degree of the model was calculated by AUC statistics. An AUC value of 0.5 or less means that the model is at a random prediction level or worse [
18], and an AUC value starting from 0.7 to 0.75 and above [
60] means that the model is likely to be useful. The results show that all the predicted results exceed the AUC threshold of 0.7, which is suitable for our study area (
Table 2).
3.2. Spatial Distribution of Recreational Landscape
The MaxENT model’s results of various RL outputs are shown in
Figure A1,
Figure A2 and
Figure A3. The Jenks classification method was employed to classify the RLs in each national park. Based on the high-value areas of GRL, TRS (23,799.02 km
2), KLM (14,005.28 km
2), QLS (5687.11 km
2), CHT (5588.50 km
2), and YTGC (4681.01 km
2) are at the top of the area rankings, each exceeding 4000 km
2. In terms of the proportion of high-value areas within the total area, regions with a larger proportion include REG (40.25%), SGRL (29.83%), and TRS (20.09%). Smaller parks with significant high-value areas include PMR (8.69%), QMLM (5.41%), CHT (5.40%), and QHL (2.59%). For HRL, the large areas of national parks such as KLM, CHT, QMLM, and TRS each have high-value areas of 5449.13 km
2, 5126.45 km
2, 4323.73 km
2, and 4080.86 km
2, respectively, all surpassing 4000 km
2. National parks with high-value HRL areas include GPD (24.42%), QHL (23.44%), GGM (14.44%), and QMLM (11.69%). The other national parks contain less than 10% high-value HRL areas. Regarding BRL, CHT (13,818.37 km
2), TRS (13,396.49 km
2), KLM (8366.96 km
2), and QLM (7457.06 km
2) each contain more than 4000 km
2 of high-quality BRL areas. Proportionally, national parks with significant BRL high-value areas include GLGS (28.55%), DXM (24.30%), REG (21.26%), QLM (17.82%), CHT (13.35%), and TRS (11.31%). All remaining national parks have less than 10% high-value BRL areas (
Figure 4).
Overall, the high-value area of GRL in the QTPNPG measures 69,081.02 km2, representing approximately 2.65% of the Qinghai–Tibet Plateau, while the high-median area is 187,764.18 km2, accounting for roughly 9.84%. The high-value area of HRL covers about 33,251.20 km2, or 1.27%, while the middle–high-value area spans 111,565.19 km2, or 4.27%. The high-value area of BRL covers around 59,348.65 km2, and the high–middle-value area extends over 163,116.99 km2, representing about 2.27% and 6.25%, respectively. Among these, the QTPNPG contains the largest area of high-quality GRL, followed by high-quality BRL, while high-quality HRL occupies the smallest area.
3.3. Spatial Distribution of Recreational Ecosystem Services
The three types of RL spaces were superimposed, and the RESV of each national park was graded by the natural break point method to obtain the spatial distribution of RESs (
Figure A4). At the same time, the area of high-value areas and their area proportions were statistically analyzed (
Figure 5). The area of the RESV of TRS (33,759.43 km
2) and its proportion (28.78%) were the largest, and its spatial distribution was mainly in the source area of the Yellow River, the east area of the Yangtze River, and the source area of the Lancang River (southeast). The second national park with a high-value area is GPD (22.81%), which is mainly distributed in the northern, central, and southern areas of the park. The REG high-value area accounted for 22.34%, mainly distributed in the north of the park and along the Yellow River. The high-value area of QHL is 21.37%, and its high-value area is mainly distributed in the eastern lake and lake region, as well as the western estuarine delta. The high-value area of KRBQ accounts for about 19.19%, which is mainly distributed in the Zadatulin area in the north of the park and the “Sacred Mountain and Sacred Lake” area in the southeast. The high-value area of QLM accounted for about 14.43%, mainly distributed in the central region; the high-value area of QMLM accounted for 14.01%, mainly distributed in the central and southwest regions along the national border; the high-value area of area of GLGM accounts for about 10.97%, which is mainly distributed linearly along the river valley. The high-value area of GGM accounts for about 10.22%, distributed in the northern part of the park. The high-value area of CHT accounted for about 9.44%, and the high-value area was mainly distributed in and around lakes. The high-value area of YTGC accounted for 8.73%, mainly distributed in the eastern Yarlung Zangbo Grand Canyon area. The high-value area of SGRL accounts for about 7.54%, which is mainly distributed in the middle of Shangri-La area and the Pudacuo area in the northeast. The proportion of KLM high-value area is about 5.99%, and the high-value area is mainly distributed in the northeast. The high-value area of PMR accounts for about 2.40%, which is mainly distributed in the north. The distribution and proportion of high-value areas indicate the distribution area of high-quality RESs in each national park. From the perspective of the QTPNPG, the proportion of high-value areas of RES in national parks in the eastern part of the Qinghai–Tibet Plateau is higher.
The three types of RL spaces were superimposed, and the RES of each national park was classified using the Jenks classification method to reveal the spatial distribution of the RESs (
Figure A4). Simultaneously, the high-value areas and their proportions were statistically analyzed (
Figure 5). TRS had the largest area (33,759.43 km
2) and proportion (28.78%) of RESs, with its spatial distribution primarily covering the source areas of the Yellow River, the eastern Yangtze River, and the Lancang River (southeast). The second national park by high-value area is GPD (22.81%), mainly in the park’s northern, central, and southern regions. REG’s high-value area makes up 22.34%, mainly along the Yellow River in the park’s northern part. The high-value area of QHL is 21.37%, primarily in the eastern lake and the western estuarine delta. KRBQ accounts for 19.19%, mostly in the Zadatulin area to the north and the “Sacred Mountain and Sacred Lake” in the southeast. The high-value area of QLM is about 14.43%, mainly in the central region. QMLM’s high-value area is 14.01%, primarily in the central and southwestern regions near the national border. GLGM’s high-value area is 10.97%, primarily along the river valley. GGM has 10.22%, mostly in the park’s northern part. CHT, at 9.44%, is mainly in and around lakes. YTGC accounts for 8.73%, primarily in the eastern Yarlung Zangbo Grand Canyon area. SGRL represents 7.54%, mainly in the central Shangri-La area and the Pudacuo region in the northeast. KLM’s proportion is 5.99%, mainly in the northeast. PMR’s high-value area makes up 2.40%, primarily in the north. The distribution and proportion of high-value areas reveal the spatial distribution of high-quality RES across national parks. Overall, the proportion of high-value RES areas is higher in the national parks of eastern Qinghai–Tibet Plateau.
3.4. Monetization of Recreational Ecosystem Services Value
The unified quantification results of the value of RESs through monetization are shown in
Figure 6. The total value and unit area value of each national park were divided into four categories (high, medium–high, medium, low) by the natural discontinuous point method (
Figure A5 and
Figure A6). The national parks with the highest total value were TRS (CNY 3.053 billion), QMLM (CNY 1.323 billion), YTGC (CNY 1.035 billion), KLM (CNY 814 million), CHT (CNY 396 million), SGRL (CNY 352 million), QLM (CNY 301 million), GPD (CNY 296 million), and QHL (CNY 242 million). KRBQ (CNY 125 million), GGM (CNY 124 million), GLGM (CNY 98 million), REG (CNY 85 million), and PMR (CNY 80 million) are the national parks with the lowest total amount. The classification of unit value shows that the national parks of high grade are SGRL (CNY 57,300/km
2) and QHL (CNY 42,300/km
2). The medium–high grades were QMLM (CNY 33,700/km
2), YTGC (CNY 29,100/km
2), GPD (CNY 28,800/km
2), and TRS (CNY 25,800/km
2). The unit value level of GGM (CNY 15,500/km
2), GLGM (CNY 13,900/km
2), and REG (CNY 12,500/km
2) was medium; the national parks with low unit value are QLM, KLM, KRBQ, CHT, and PMR, all of which have values of less than CNY 10,000/km
2. The total monetary value of RESs in the QTPNPG is CNY 8.323 billion, and the average monetary value per unit area is CNY 20,200/km
2 (
Figure 6). The total monetary value of RESs in the QTPNPG is significantly different from the monetary value per unit area, and the difference between the maximum and the minimum monetary value is about 38 times. The amount of money per unit area varies by about 21 times.
The unified quantification results for RESV via monetization are displayed in
Figure 6. The total and unit area values of each national park were classified into four categories (high, medium–high, medium, low) using the natural breaks classification method (
Figure A5 and
Figure A6). The national parks with the highest total values were TRS (CNY 3.053 billion), QMLM (CNY 1.323 billion), YTGC (CNY 1.035 billion), KLM (CNY 814 million), CHT (CNY 396 million), and SGRL (CNY 352 million). QLM (CNY 301 million), GPD (CNY 296 million), and QHL (CNY 242 million) followed. The parks with the lowest total values included KRBQ (CNY 125 million), GGM (CNY 124 million), GLGM (CNY 98 million), REG (CNY 85 million), and PMR (CNY 80 million).
In terms of unit values, the highest-ranked national parks were SGRL (CNY 57,300/km2) and QHL (CNY 42,300/km2). Those in the medium–high category included QMLM (CNY 33,700/km2), YTGC (CNY 29,100/km2), GPD (CNY 28,800/km2), and TRS (CNY 25,800/km2). The unit values of GGM (CNY 15,500/km2), GLGM (CNY 13,900/km2), and REG (CNY 12,500/km2) fell in the medium range. National parks with low unit values, all below CNY 10,000/km2, were QLM, KLM, KRBQ, CHT, and PMR.
The total monetary value of RES in the QTPNPG is CNY 8.323 billion, with an average unit value of CNY 20,200/km2. However, significant differences exist between total monetary value and unit area value across the parks, with the maximum and minimum values differing by approximately 38 times and the unit values varying by around 21 times.
3.5. Recreational Landscape Correlation and Pattern Evaluation
The landscape aesthetic service refers to the pleasure people derive from the scenic beauty of natural areas and landscapes [
61]; this is often linked to the quality and form of the RLs. The landscape pattern influences recreational activities. Research indicates that factors like water surface area, sidewalk width, recreational area functionality, plant composition, color diversity, and species richness positively impact the visual quality of urban landscapes [
62]. The relationship between landscape and recreation is complex. Various methods such as land assessment, impact analysis, spatial behavior analysis, landscape quality assessment, and landscape evaluation help analyze this relationship [
63]. On a microscale, like campuses, urban parks, and leisure corridors, areas with many water features and high vegetation coverage tend to be more specific and hold greater aesthetic value [
40]. On the mesoscale, which includes urban green belts, natural lakes, and forests, preferences for altitude, cultural heritage, and specific flora and fauna remain consistent. Areas with high recreational potential for diverse user groups also tend to support varied landscapes like forests or mosaic land use [
64]. Evaluating the recreational landscape of large national parks is intertwined with micro- and mesoscale composition, while also revealing unique characteristics. From a psychological perspective, we believe that RL diversity and pattern characteristics directly impact the recreational experience. High-quality RL diversity and areas with strong connectivity and aggregation display greater landscape aesthetic value, making them ideal for recreation and priority zones for such activities.
The results of Bivariate Moran’s I showed that the correlation is higher (>0.2) and the confidence is 99% (
Figure 7): GRL and BRL of GPD (0.486), GRL and HRL of GPD (0.582), BRL and HRL of GPD (0.365), GRL and HRL of REG (0.217), GRL and BRL of REG (0.215), GRL and BRL of REG (0.215), and GRL and BRL of GLGM (0.241), In addition, the correlation ranges from 0.1 to 0.2 (99% confidence): GRL and BRL of QHL (0.136), GRL and BRL of QLM (0.100), HRL and BRL of QLM (0.159); GRL and HRL of QMLM (0.105), GRL and BRL of QMLM (0.143); GRL and HRL of SGRL (0.125), GRL and BRL of SGRL (0.159); HRL and BRL of REG (0.198); HRL and BRL of GLGM (0.148). There is a certain correlation for some areas of high-quality combined RLs in these national parks. The correlation between the remaining landscapes was less than 0.1, indicating a high degree of separation between the RLs, the spatial separation of high-quality RLs, and high-quality RLs mainly distributed in a single form (
Figure A7).
The high-value recreational landscape area represents the supply area of high-quality recreation services in the national park, and it is the area with the most landscape aesthetic services in the region. The distribution pattern of the recreational landscape in the high-value area affects the recreation experience of the recreational users and the recreational development by the managers. We evaluated the landscape pattern (LPI, PCI, SI) of the high-value areas with the superposition of the three types of recreational landscape spaces, and marked the six optimal landscape patterns of each type in the national park group (
Table A4). The LPI, PCI, and SI of GLGM, KRBQ, QHL, and TRS all belong to the best six items (
Figure 8), indicating that their regions have a large area of high-quality landscapes, which are relatively clustered and have high connectivity. GPD has a large area of high-quality landscape and relatively concentrated high-quality recreational landscape; GGM has a relatively large area of high-quality landscape; the high-quality landscapes of KLM and YTGC have good connectivity; the concentration degree of the REG high-quality landscape is very good and there is a pattern with the characteristics of other landscapes. The landscape patterns of PMR, QLM, CHT, SGRL, and QMLM did not perform well.
Overall, GRL and BRL exhibited a strong positive spatial correlation among the RL types in the QTPNPG, reflecting the spatial interdependence of the biological and geomorphic environments, that are influenced by the geographic environment. In terms of RL, the coordination and tradeoff results highlight the combinations of RL types that recreation users can experience in each national park. Visitors to the QTPNPG are most likely to encounter the high-quality combination of BRL and GRL, followed by BRL and HRL. The combination landscape areas in the eastern region of the QTPNPG are more expansive than those in the western region. In terms of discrete distribution, high-quality landscapes are scattered and predominantly feature individual high-quality RLs. Based on the value of the RES and RL evaluations, TRS, QMLM, QHL, GPD, YTGC, and SGRL emerge as the priority regions for recreational activities in the QTPNPG.
4. Discussion
4.1. Suggestions for the Sustainable Development of QTPNPG
The study aims to gain insight into RESs in the QTPNPG, which has important implications for higher-quality recreational development on the Tibetan Plateau. The QTPNPG has a unique natural landscape and cultural heritage, making it an increasingly popular destination for recreational activities. Recognizing the importance of sustainability is essential to protect the ecological integrity of this fragile ecosystem while maximizing its socioeconomic benefits. RES mapping and monetization within the QTPNPG provides valuable insights into the distribution and economic value of recreational opportunities in the region. By incorporating these findings into recreational planning initiatives, stakeholders can better prioritize resource allocation, infrastructure development, and visitor management strategies to enhance the overall recreation experience while minimizing environmental impact. Based on the research results, the following specific suggestions are put forward to guide the sustainable recreational development of the QTPNPG:
Enhancing landscape connectivity: Prioritize the preservation and restoration of landscape connectivity to facilitate the movement of wildlife and enhance recreational experiences, such as establishing wildlife corridors and greenways.
Balancing conservation and recreation: Implement measures to balance conservation objectives with recreational demands, ensuring that visitor activities are compatible with ecosystem protection goals. This may involve zoning strategies, visitor-carrying capacity assessments, and the establishment of designated recreation zones.
Promoting community engagement: Foster community involvement in recreational planning and management processes to ensure that local perspectives and traditional knowledge are integrated into decision making. This can promote sustainable livelihoods and cultural preservation while enhancing visitor experiences.
Monitoring and adaptive management: Establish robust monitoring programs to track the ecological and socioeconomic impacts of recreational activities, allowing for adaptive management strategies to address emerging challenges and opportunities.
By incorporating these recommendations into policy development and management practices, stakeholders can work to achieve a harmonious balance between conservation and recreation, ensuring the ecological conservation and economic benefits of this unique region.
4.2. Limitations and Prospects
RESs are frequently mentioned but are rarely comprehensively assessed within the ES framework, particularly in terms of value monetization [
65,
66]. Previous studies have shown that studies on RESs or CESs are often derived solely from land use data or biological cover data such as LULC, NDVI, and NPP [
54]. However, given the vast expanse of the Qinghai–Tibet Plateau and its unique geographical environment, these methods are not suitable. Our study, based on known recreational data points, classified RL resources into GRL, HRL, and BRL, integrating various recreational resource types to generate environmental data. The MaxENT model was employed to simulate RL supply regionally and more objectively, leading to a more accurate assessment of the RESs. This effectively addresses the shortcomings of previous studies and advances the research methods for RESs.
Although our work quantifies the supply, distribution, and monetization values of RESs in the QTPNPG from the perspective of RL supply and offers valuable insights for scientific research and practical applications, there are still limitations. We divided RL supply into three categories, but while combining RLs into RESs, we did not further differentiate between the value disparities among RL types, instead opting for an aggregate sum. Given the significant geographical differences among the national parks in the QTPNPG, we believe this impact can be mitigated by scale considerations. In future assessments of the internal recreation value of individual national parks, it is important to consider the unique value differences of each RL type. Our research introduces a new methodology for calculating the monetization of RESs. However, by using the average annual tourism income of Qinghai Lake National Park over the past decade as the baseline for recreational value, we may have overlooked the influence of human factors like cultural attractions and tourism facilities [
39]. While most visitors primarily seek natural landscapes for recreation, the resulting monetary valuation may be slightly higher than the actual value.
Conducting recreation suitability assessments and planning for individual national parks is both meaningful and essential, particularly for those with high RESV per unit area, expansive high-quality landscape areas, and favorable landscape pattern evaluations. The ecological environment of the Qinghai–Tibet Plateau is fragile, with climate and geological conditions as primary constraints on recreational activities and development. Moving forward, we should perform scientific evaluations and planning for recreational functionality based on key indicators.
5. Conclusions
This study employed the MaxENT model to comprehensively map and monetarily assess the RESs of the QTPNPG by integrating recreation resource data and environmental data. The research findings revealed the distribution characteristics of GRL, BRL, and HRL, as well as their specific impacts on the quality and spatial distribution of RES supply. The landscape features of GRL, including the unique geological forms and topographic landscapes of the Qinghai–Tibetan Plateau such as the Qomolangma Mount and the Yarlung Tsangpo Grand Canyon, offer unparalleled opportunities for activities like mountaineering, ice climbing, and ecotourism photography. These landscapes not only possess high visual aesthetic appeal for tourists but also hold significant importance for ecotourism and environmental education. The unique landscapes of BRL are manifested in the diverse flora and fauna species endemic to the Qinghai–Tibetan Plateau, such as Tibetan antelopes, snow leopards, and highland-specific plants. These biodiversity resources provide rich content for nature observation and ecotourism while emphasizing the importance of biodiversity conservation. The landscape types of HRL encompass iconic features such as Qinghai Lake and the headwater rivers of the Yangtze and Yellow Rivers, which hold profound cultural significance as national symbols. These sites serve as venues not only for leisure activities like birdwatching, lakeside hiking, and river drifting but also as embodiments of cultural heritage, adding depth to the recreational experiences offered. The high-value areas identified for GRL, BRL, and HRL span 69,081.02 km2, 59,348.65 km2, and 33,251.20 km2, respectively, representing 2.65%, 2.27%, and 1.27% of the QTPNPG’s total area. These areas underscore the significant roles of these landscapes in supplying specific ecosystem services tailored to diverse recreational needs while enhancing the overall quality of recreational experiences. Through monetary assessment, we estimated the total value of RES in the QTPNPG to be CNY 8.323 billion, with an average value per-unit area of CNY 20,200/km2. This quantified result provides a basis for policymakers to optimize resource allocation and investment to promote sustainable leisure development. The recommendations proposed in this study, including enhancing landscape connectivity, balancing conservation with recreational demands, promoting community participation, and monitoring and adaptive management, aim to achieve a harmonious balance between recreational activities and ecological environment in the QTPNPG. These suggestions contribute to advancing sustainable recreational development in the QTPNPG, realizing the triple goals of economic, social, and environmental development.