Aligning Tourist Demand with Urban Forest Ecosystem Services: Sustainable Development Strategies for Enhancing Urban Tourism Resilience in Kunming

Abstract

With the increasing importance of urban green spaces in leisure, ecology, emergency management, and other functions, urban forest parks play a key role in enhancing urban tourism resilience. Tourists are closely related to this, but current research lacks discussion on the sustainable development of urban forests and tourism resilience from the perspective of tourist demand. Therefore, this study took Kunming Xishan Forest Park as an example, conducted a questionnaire survey of 385 tourists, and identified tourist demands and weights through in-depth analysis using the KANO model and AHP. The results data show that among the 23 demand indicators across five dimensions, six are must-be qualities, eight are one-dimensional qualities, six are attractive qualities, and three are indifferent qualities. Based on the AHP analysis, we further investigated the weight of each demand indicator. The results of this study not only provide practical support and strategic guidance for the spatial planning and design of urban forests, thereby enhancing the sustainable development of urban tourism resilience, but also contribute to theories of urban tourism resilience and offer a reference source for other cities with similar aspirations.

1. Introduction

As social, financial, and educational centers, cities attract residents from all over the world. There are data showing that nearly 80% of the world’s population currently lives in urban areas [1]. With the development of cities, urban tourism has gradually emerged since the 1990s [2] and is one of the fastest-growing parts of tourism in economically developed countries [3]. In recent years, governments at all levels and scholars in various countries have increasingly recognized the importance of urban forests [4,5,6,7]. As an important part of urban ecosystems, urban forests play an increasingly important role in responding to climate change, providing ecosystem services, improving the tourist experience, and enhancing the adaptability of urban tourism [1,8]. They are considered an effective tool for mitigating ecological, social, and economic problems caused by rapid urbanization and population expansion [9].

However, as an industry highly sensitive to external shocks [10], tourism is facing increasingly frequent crises and uncertainties, particularly under urbanization, climate change, and global public health security challenges. According to the United Nations World Tourism Organization (2020), the impact of COVID-19 had resulted in a global decline of over 700 million tourists and economic losses exceeding $730 billion by August 2020—more than eight times the losses incurred during the 2009 financial crisis [11]. In this context of high uncertainty, tourism destinations urgently need to develop stronger resilience and recovery capabilities to respond to potential emergencies and systemic risks. Tourism resilience, a new approach to measuring the ability of tourism systems to maintain their functions, structures, and order in the face of disruptions, has gradually become a focus of research and practice. It is often portrayed as the savior of global and local tourism [12].

At the same time, with the rapid development of urban tourism activities and the ever-changing demands of tourists, the shortcomings of traditional urban tourism management and planning models in responding to sudden risks and ensuring safety have gradually become apparent, making it difficult to meet diverse demands. Especially in the post-pandemic era, tourists’ demands are more focused on safety, flexibility, and personalization, which requires tourism to be more adaptable and variable. Therefore, how to coordinate the balance between the demands of different tourists and ecological functions through reasonable design and management measures on the basis of ensuring ecosystem services has become an important issue that cannot be ignored in improving the resilience of urban tourism.

1.1. Urban Forest Ecosystems and Tourism

Urban forests, green spaces that combine ecological functions with urban attributes, have natural advantages in terms of the development of ecotourism. In the 1960s, the concept of urban forests was formally proposed in the United States [13]. In 1962, the term “Forest City” was first used [14] in the 1970s and 1980s; Asia and other regions proposed the concept of “trees outside forests” [15,16]. This was defined as trees, forests, green spaces, and related non-biological, biological, and cultural components extending from the core of a city to the urban fringe [5,17,18,19] and is an area of growing concern in the urbanized world. In 1992, the concept of urban forests was first introduced in China. With the establishment of the National Forestry Research Institute, the government began to promote it at the national policy level, which gradually attracted the attention of many scholars.

It is worth noting that many studies have confirmed the importance of forest ecosystems for tourism activities. As a form of ecotourism, forest tourism has significant benefits for promoting physical and mental health [20,21]. Over the past decade, scholars from various fields have explored this topic [22,23,24,25]. In a comparative study on stress reduction in urban forests (simple size = 47), 74% of respondents believed that botanical gardens helped restore vitality and alleviate stress and anxiety. Meanwhile, 93% of respondents believed that extensive vegetation cover created a visual and physical barrier from the outside world, allowing them to focus more on themselves [26] and providing significant psychological healing benefits. In addition, trees in urban forests help reduce air temperature, thereby improving air quality and preventing air pollution and ozone depletion [27].

In recent years, this form of tourism has gained increasing popularity, especially urban forest parks which have become important venues for people to engage in forest ecotourism [28]. Holling et al. (1973) also pointed out that this type of tourism can not only alleviate conflicts between conservation and development and reduce the impact of tourists but also provide beneficial and positive socio-economic participation for local residents [29].

1.2. Tourism Resilience and Sustainable Development

“Resilience” was initially applied in the field of physics, referring to the ability of an individual or system to return to its original state or equilibrium after being subjected to disturbances or shocks. Halling first introduced the concept into the field of ecology [29], and it has since gained widespread recognition in both natural and social sciences. Subsequently, many scholars introduced resilience theory into the field of tourism [30,31], and “tourism resilience” gradually became an important theme in tourism research [32], where it is used to understand the adaptability of destinations to global and local challenges, crises, and disasters [33]. However, compared with other fields, this concept is relatively limited in the field of tourism and was applied relatively late on [34].

Hall et al. (2017) proposed in their study that tourism resilience can be examined from three aspects: individuals, organizations, and destinations [35]. Prayag (2020) also put forward a similar view, dividing tourism resilience into tourism systems, tourism destinations, and tourism communities, and conducting a detailed discussion from the macro, meso, and micro levels [36]. Qiao et al. (2023) summarized the concept of tourism resilience in detail, arguing that tourism resilience is a complex system within a certain geographical area whose main function is to maintain the stable development of tourism. It consists of three subsystems: economic resilience, social resilience, and environmental resilience. It manifests itself as the ability to maintain, reorganize, learn, and adapt when the tourism economy is impacted by sudden internal and external events within a specific time frame [37]. Wang et al. (2020) further added that tourism’s economic resilience not only includes resistance and recovery but also the ability to restructure and renew [38].

In recent years, the concepts of “resilience” and “sustainability” have been the focus of scholars in the field of tourism. Tourism resilience plays an important supporting role in discussions on sustainable tourism development because it not only covers the core elements of sustainability but also recognizes the significant impact of different environments on the adaptive capacity of communities and the maintenance of sustainable tourism development [31]. Saarinen and Gill (2019) argue that resilience is inherently linked to sustainability [39] and that highly resilient tourism systems can maintain relatively stable development in the face of various challenges, thereby achieving long-term balance between tourism’s economic, social, and environmental benefits. This view is consistent with Schianetz and Kavanagh (2008), who argue that resilience is a key element of sustainability [40], especially in tourism. Tourists are also becoming increasingly aware of environmental and sustainability issues [41] and prefer products that are nature-based, offer learning opportunities, follow sustainable development principles, and are environmentally responsible [41,42].

As mentioned before, the ecosystem services provided by urban forests not only benefit tourists’ physical and mental health but also play a crucial role in maintaining the stability of tourism systems [43,44]. Daniel and Sven (2024) point out that high forest coverage can mitigate thermal stress and air pollution, thereby enhancing a destination’s resilience to climate impacts [45]; well-designed spatial structures and green infrastructure can disperse tourist pressure, preventing overload at single nodes [46]. These ecosystem services inherently construct the ecological foundation of tourism resilience, endowing destinations with stronger buffering capacity and recovery potential when confronting external shocks or internal pressures [47,48]. Within this context, tourists serve as a pivotal entry point connecting ecosystem services to tourism resilience, offering clearer insights into how ecosystem services translate into practical factors supporting tourism resilience.

1.3. Tourist Demand

Some scholars have suggested that tourists are one of the key subjects in micro-level discussions on tourism resilience [12,49]. Tourist demand, as an important factor driving tourist activities, refers to the psychological, physiological, emotional, cultural, and other deficiencies or desires expressed in the process [50], such as special features, transportation facilities, and tourism products [51]. Tourist demand varies with age, income, cultural background, and travel purpose. In modern tourism, tourists are increasingly inclined to choose destinations that can provide personalized and unique experiences. Studies have shown that tourist demand is influenced by various external factors such as society, economy, environment, and technology, and will continue to change over time, with strong time sensitivity and regional differences [50].

Especially after COVID-19, tourists’ demands for safety, hygiene, and travel flexibility have changed significantly [52]. With the changing global tourism trends, tourists’ demands are no longer limited to traditional natural landscapes and entertainment activities. Demands for sustainability and ecological protection have become new research hotspots. Understanding tourist demand is key to understanding tourist consumption behavior, and information acquisition is the basis for better understanding tourist demand. Through analyzing tourist demand, it is possible to better guide venue planning and design, highlight key points, and cater to tourists [51].

Experiences provided by natural environments have always been a driving force for people to engage in leisure, entertainment, education, and other forms of tourism [53]. Studies have shown that in recent years, short-distance tourism and urban suburban tourism have experienced an upward trend, with people increasingly keen to enjoy the beauty of nature and the pleasure of being in it without having to travel far [54]. Urban forest parks are important green spaces in cities. They not only provide leisure and entertainment venues for urban residents, but also play an irreplaceable role in protecting the urban ecological environment, protecting biodiversity, promoting social and cultural development, and enhancing residents’ sense of well-being [55].

Kunming has rich forest resources and a unique plateau ecological environment [56]. Among them, Xishan Forest Park integrates ecological landscape, cultural landscape, and leisure and recreation [57], and is an important carrier of Kunming’s urban forest ecosystem services. However, Xishan Forest Park still has problems in terms of service provision for tourists [58], which limits the overall resilience of its tourism system. Moreover, current research on urban forests mostly focuses on technical measurements [59,60], management models [6,17,61], resource assessment [62,63], and the impact on consumer behavior and purchasing decisions regarding eco-tourism consumption [41]. There is little discussion of urban forests and their tourism resilience from a micro level, i.e., from the perspective of tourist demand [36,64]. Therefore, this study takes Kunming Xishan Forest Park as an example and attempts to use the semi-structured interview and KANO model to analyze deeply the tourists’ demand for urban forest ecosystem services, providing theoretical support and practical guidance for improving the sustainable development of urban tourism resilience. The KANO model, first proposed by Noriaki Kano in 1984, which is used to analyze user demands and determine product requirements [65], is highly applicable to this study for exploring tourist demands. The following (see Figure 1) presents the conceptual framework of this study:

2. Materials and Methods

This study uses a mixed-method research design [66], combining semi-structured interviews with KANO questionnaires to explore tourists’ demands for urban forests and their demands when faced with risks. In the qualitative stage, initial demand indicators for tourists are obtained through semi-structured interviews, and these indicators will be used as items in the subsequent questionnaire design. Next, KANO analysis and AHP were combined to accurately determine the categories and importance of tourist demands in Xishan Forest Park, providing a useful reference for improving urban tourism resilience. This method provides a comprehensive framework (see Figure 1).

2.1. Study Area and Target Population

We collected data on tourists in Xishan Forest Park. It is located in the western suburbs of Kunming City, Yunnan Province, China. It is composed of peaks such as Biji Mountain, Huating Mountain, Taihua Mountain, Taiping Mountain, and Luohan Mountain, rising gradually from north to south (see Figure 2). The highest peak reaches an elevation of 2507.5 m, with a total area of 889 hectares [67]. Except for Luohan Mountain, the rest are dense secondary forests, with a very distinct vertical forest zone pattern as altitude increases. The Xishan area has a rich and concentrated plant life, with 167 families, 594 genera, and 1086 species of shrubs, trees, and other plants. There are also more than 90 species of medicinal plants [58], as well as some rare tree species such as Taiwan cypress, goose ear larch, Chinese sumac, star anise, Yunnan purple wisteria, Yunnan camphor, and long-handled camphor trees [57]. Xishan Forest Park is a comprehensive and multifunctional scenic spot, including religious sites such as Huating Temple, Taihua Temple, Sanqing Pavilion, and Longmen Grottoes; cultural buildings such as Sheng’an Shrine and Xu Xiake Memorial Hall; and many modern celebrity cemeteries.

2.2. Semi-Structured Interview Process

In order to design the KANO questionnaire, we conducted semi-structured interviews with 12 tourists [68] from Xishan Forest Park through convenience sampling from April 23 to 29, 2025, focusing on tourists’ demands for urban forests and their resilience to external risks. Supported by relevant references [69,70,71,72], the interview questions centered on tourists’ authentic experiences during visits to forest parks, focusing on their layers of attention and expectations when facing sudden risks. After the interviews, researchers conducted coding analysis on the collected data and identified five dimensions—experience and accessibility, orientation and information support, safety and emergency assurance, alternative paths and flexible space, and cultural identity and ecological co-construction—as well as 23 specific indicators (see Figure 3).

2.3. Questionnaire Design

According to the latest data released by the Kunming Culture & Tourism Bureau, the number of tourists visiting Xishan Forest Park during the 2025 May Day holiday was close to 1.39 million. Based on Raosoft’s sample size calculation, a sample size of 385 tourists is sufficient. Considering the uncontrollable factors in the questionnaire collection process, this study will distribute 20% more questionnaires than the sample size. This study strictly follows ethical standards. Before the questionnaire began, all participants, including tourists who participated in semi-structured interviews, were provided with detailed research explanations and informed consent forms, clearly informing participants that their data would be treated anonymously and used only for academic research.

The first section of this questionnaire includes basic information such as gender, age, educational attainment, marital status, and income, comprising 6 questions. The second part is a KANO questionnaire containing 23 specific demand indicators. Each demand indicator question consists of positive and negative questions to understand tourists’ views on each demand indicator. The questionnaire uses a 5-point Likert scale for scoring, with dissatisfied, tolerable, neutral, should be like this, and very satisfied corresponding to 1–5 points, respectively. The questionnaire design is shown in

Table 1. Design form of KANO questionnaire.

Question:	Very Satisfied	Should Be Like This	Neutral	Tolerable	Dissatisfied
How do you feel if it has this function?
How do you feel if it doesn’t have this function?

. In addition, before the official distribution of the questionnaire, we randomly selected 45 tourists from Xishan Forest Park to conduct a pilot test and collected 42 valid questionnaires. Reliability analysis showed that Cronbach’s α coefficient was 0.941 > 0.8, indicating high reliability, and the data met the research objectives. The KMO value was 0.914 > 0.8, and the Bartlett sphericity test chi-square value was 20,658.048, with a df value of 253 and a significance value of 0.000 < 0.05, indicating that the data were valid.

2.4. Data Collection

In this study, convenience sampling was adopted during data collection. Survey stations were established at the main entrances and key attractions (such as Huating Temple and Longmen) within Xishan Forest Park, where tourists were randomly invited to complete questionnaires. To ensure sample diversity, surveys were conducted across different dates and time periods, thereby covering as broad a range of tourist groups as possible. All data collection was conducted after obtaining ethical approval. The entire process fully respected the rights of participants, who could stop filling out the questionnaire at any time without any restrictions [73]. In order to ensure a valid sample size of 385 tourists, a total of 462 questionnaires were distributed for this study. Participants needed about 5–10 min to complete the questionnaire, which was anonymous and collected by scanning a QR code using the Wen Juanxing. After the questionnaire was released, researchers could immediately access participants’ feedback, enabling the timely acquisition of questionnaire data and ensuring data collection security. To enhance participation rates and express gratitude, each participant received a random red envelope via Wen Juanxing after submitting the questionnaire.

2.5. Data Analysis Approach

In order to obtain accurate tourist demand, we combined two analytical methods: KANO and Analytic Hierarchy Process. First, we tested the validity and reliability using SPSS 26.0. Then, we classified tourist demand using KANO analysis and finally determined the weight of each indicator of demand using AHP.

Noriaki Kano divided product quality into five categories: the must-be quality (M), one-dimensional quality (O), attractive quality (A), indifferent quality (I), and reverse quality (R) [21]. Among them, the must-be quality is the basic demand of users for the products or services provided and is the attribute or function that users consider to be essential for the product. The one-dimensional quality is what users want to receive and can also be described as the itch that users want to scratch, which is a demand that reflects competitiveness. The attractive quality refers to demands that are not overly expected by users, representing the potential demands of users, which is extremely beneficial for improving user loyalty. The indifferent quality refers to demands that have no impact on users regardless of whether they are provided or not. The reverse quality refers to a demand that many users do not have at all, and providing it will actually decrease user satisfaction.

This approach is used in various fields, such as medicine and services, and is also widely used in tourism, such as accommodation, adventure and outdoor entertainment, attractions, entertainment, tourism services, and tourism trade [74]. The KANO model determines attribute classification by calculating the better–worse coefficient, and the calculation formula is as follows:

$$\mathrm{B}\mathrm{e}\mathrm{t}\mathrm{t}\mathrm{e}\mathrm{r}/\mathrm{S}\mathrm{I}={\displaystyle \frac{(\mathrm{O}+\mathrm{A})}{(\mathrm{M}+\mathrm{O}+\mathrm{A}+\mathrm{I})}}$$

(1)

$$\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{e}/\mathrm{D}\mathrm{S}\mathrm{I}=-1\times {\displaystyle \frac{(\mathrm{O}+\mathrm{M})}{(\mathrm{M}+\mathrm{O}+\mathrm{A}+\mathrm{I})}}$$

(2)

When the absolute values of the better coefficient and worse coefficient are both greater than 0.5, the quality is one-dimensional (O). When the absolute values of both the better coefficient and the worse coefficient are less than 0.5, it is classified as an indifferent quality (I); when the better coefficient is greater than 0.5 and the absolute values of the worse coefficients are all less than 0.5, it is classified as an attractive quality (A); and when the better coefficient is less than 0.5 and the absolute values of the worse coefficients are greater than 0.5, it is classified as a must-be quality (M).

The Analytic Hierarchy Process (AHP) was developed by Saaty to address decision-making problems in complex and multi-criteria situations [75]. It assists in making decisions in decision-making scenarios with several interrelated and often conflicting criteria and determines the priority of decision criteria within the context of decision objectives [76]. The basic steps include establishing a hierarchical structure model, constructing a comparison matrix, calculating weight vectors and performing consistency checks, and calculating composite weight vectors. It is considered to be highly flexible and can be used as a stand-alone tool or in combination with other tools [75]. Due to its limitations, the KANO model cannot accurately assess the importance of different demands. Therefore, in this study, the two analysis methods were combined to not only classify tourist demands but also identify the priority of each demand point. This study used AHP to identify the weights of the indicators. The specific process and steps are as follows:

(1) Normalize the matrix with the formula

$$b_{ij}={\displaystyle \frac{a_{ij}}{\sum _{i=1}^n{a}_{ij}}}\left(i,j=\mathrm{1,2},\dots ,n\right)$$

(3)

where $a_{ij}$ represents the data in row i and column j of the judgment matrix A, and $b_{ij}$ represents the data in row i and column j of the normalization matrix.

(2) Sum the elements of the matrix as follows:

$${\overline{w}}_i=\sum _{j=1}^n{b}_{ij}\left(i,j=\mathrm{1,2},\dots ,n\right)$$

(4)

(3) For $\overline{w_i}$ in the above equation, normalize the calculation

$$w_i={\displaystyle \frac{{\overline{w}}_i}{\sum _{i=1}^n{\overline{w}}_i}}\left(i=\mathrm{1,2},\dots ,n\right)$$

(5)

where $w_i$ is the weight of the i-th indicator.

(4) Calculate the maximum eigenvalue of the judgment matrix A as follows:

$${\lambda }_{max}={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{\displaystyle \frac{{\left(AW\right)}_i}{W_i}}$$

(6)

where n is the order of the matrix, A is the judgment matrix, $w_i$ is the weight of the i-th indicator, and ${\lambda }_{\max }$ is the maximum eigenvalue of the judgment matrix A.

(5) Test CI as follows:

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(7)

(6) Test CR as follows:

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

(8)

If CR < 0.1, the consistency test is satisfied; otherwise, modify the judgment matrix.

3. Results

3.1. Results of KANO

In order to ensure an effective sample size, we distributed 20% more questionnaires (462 in total) and received 393 responses, for a response rate of 85.1%. According to the demographics of the final valid questionnaires, in terms of gender distribution, males accounted for 43.9% and females accounted for 49.9% of the total sample. In terms of age composition, tourists aged 18–35, 50–60, and over 60 accounted for over 80% of the sample, indicating that urban forest parks are more popular among young and elderly tourists. Other results are shown in

Table 2. Results of demographic statistics.

Characteristics		Number	Frequency (%)
Gender	Male	184	43.9%
	Female	209	49.9%
Age	18–35	136	34.6%
	36–49	63	16.0%
	50–60	101	25.7%
	60 above	93	23.7%
Educational	High school and below	63	16.0%
	Junior college	91	23.2%
	Undergraduate	207	52.7%
	Postgraduate and above	32	8.1%
Marital status	Unmarried	179	45.5%
	Married	192	48.9%
	Divorced	21	5.3%
Occupation	Student	78	19.8%
	Enterprise staff	113	28.8%
	Staff of state units	64	16.3%
	Freelancer	52	13.2%
	Retired	86	21.9%
Annual Income (RMB)	3000–5000	124	31.6%
	50,000–7000	153	38.9%
	7000–100,000	71	18.1%
	100,000 or more	45	11.5%

.

Based on the formula for calculating the better coefficient and worse coefficient, we can determine the attributes of each demand indicator. The KANO results are shown in

Table 3. KANO results of tourist’s demand.

Indicators	Dimension	A (%)	O (%)	M (%)	I (%)	R (%)	Q (%)	Attribute	Better	Worse
A1	Experience and Accessibility	25.19	18.32	36.64	13.74	6.11	0.00	M	0.4634	−0.5854
A2		23.66	19.85	32.82	18.32	2.29	3.05	M	0.4597	−0.5565
A3		15.27	19.08	39.69	22.14	3.82	0.00	M	0.3571	−0.6111
A4		22.90	34.35	21.37	15.27	5.34	0.76	O	0.6098	−0.5935
A5		16.03	19.08	35.11	25.95	2.29	1.53	M	0.3651	−0.5635
A6		16.79	36.64	16.79	25.95	0.76	3.05	O	0.5556	−0.5556
B1	Orientation and Information Support	36.64	19.85	21.37	16.79	5.34	0.00	A	0.5968	−0.4355
B2		32.82	16.79	28.24	15.27	4.58	2.29	A	0.5328	−0.4836
B3		19.08	21.37	16.79	41.22	1.53	0.00	I	0.4109	−0.3876
B4		19.85	38.17	16.03	21.37	3.05	1.53	O	0.608	−0.568
C1	Safety and Emergency Assurance	19.08	39.69	18.32	19.08	3.82	0.00	O	0.6111	−0.6032
C2		19.85	19.85	35.11	19.08	6.11	0.00	M	0.4228	−0.5854
C3		13.74	16.03	42.75	22.14	5.34	0.00	M	0.3145	−0.621
C4		9.16	41.98	22.14	22.14	4.58	0.00	O	0.536	−0.672
C5		32.82	23.66	21.37	19.85	2.29	0.00	A	0.5781	−0.4609
D1	Alternative Paths and Flexible Space	12.98	35.11	25.19	19.85	6.11	0.76	O	0.5164	−0.6475
D2		32.82	22.90	22.90	16.03	2.29	3.05	A	0.5887	−0.4839
D3		38.17	23.66	16.03	18.32	3.82	0.00	A	0.6429	−0.4127
D4		13.74	39.69	13.74	26.72	2.29	3.82	O	0.5691	−0.5691
E1	Cultural Identity and Ecological Co-construction	39.69	13.74	21.37	19.85	5.34	0.00	A	0.5645	−0.371
E2		22.14	32.06	19.08	21.37	3.05	2.29	O	0.5726	−0.5403
E3		25.19	15.27	19.08	35.11	3.82	1.53	I	0.4274	−0.3629
E4		16.79	22.90	15.27	39.69	3.05	2.29	I	0.4194	−0.4032

.

The results show that six items are must-be qualities, eight items are one-dimensional qualities, six items are attractive qualities, three items are indifferent qualities, and there are no reverse qualities. In addition, by calculating the better–worse coefficient of each demand, we can construct the following quadrant diagram (see Figure 4):

The first quadrant is for one-dimensional qualities, with relatively high absolute values for better and worse. Tourist satisfaction varies greatly. If the park provides or improves the service quality or functions in this quadrant, tourist satisfaction will increase; otherwise, satisfaction will decrease. The demand in the second quadrant belongs to attractive qualities: the absolute value of better is high, and the absolute value of worse is low. If the park does not provide services in this quadrant, tourist satisfaction will not decrease, but if it does, satisfaction will increase significantly. The demand in the third quadrant belongs to indifferent qualities: the absolute values of better and worse are both low. Tourist satisfaction will not change because tourists do not care whether these services or functions are provided or not. The demand in the fourth quadrant belongs to must-be qualities: the absolute value of better is low, and the absolute value of worse is high. When the park provides this function, tourist satisfaction will not increase; if it does not, tourist satisfaction will decrease significantly.

3.2. Results of AHP

Even for the same dimension, tourists will still have different levels of satisfaction. In order to accurately analyze tourists’ satisfaction with each demand, we used AHP to compare them in pairs, constructed a comparison matrix, and calculated the weight of each demand according to the formula. Indifferent quality refers to the neutral attitude of the respondents towards the demand, so B3, E3, and E4 were not analyzed. Based on the KANO results, we constructed an indicator evaluation system (see

Table 4. Evaluation system for indicators.

Dimension	Indicators
Must-be quality	A1
	A2
	A3
	A5
	C2
	C3
One-dimensional quality	A4
	A6
	B4
	C1
	C4
	D1
	D4
	E2
Attractive quality	B1
	B2
	C5
	D2
	D3
	E1

).

After scoring the importance of each indicator, we obtained a judgment matrix. We calculated the weight values for must-be quality, one-dimensional quality, attractive quality, and all secondary demand indicators, with the results shown in

Table 5. Random consistency indicator.

n	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RI	0	0	0.52	0.89	1.12	1.26	1.36	1.41	1.46	1.49	1.52	1.54	1.56	1.58	1.59

,

Table 6. Weight of first-level quality indicator.

	Must-Be Quality	One-Dimensional Quality	Attractive Quality	Weight
Must-be Quality	1	2	3	0.4564
One-dimensional Quality	1/2	1	3	0.3032
Attractive Quality	1/3	1/3	1	0.1461

,

Table 7. Weight of secondary indicators of must-be quality.

	A1	A2	A3	A5	C2	C3	Weight
A1	1	3	4	7	2	5	0.3837
A2	1/3	1	2	4	1/2	3	0.1582
A3	1/4	1/2	1	3	1/3	2	0.1008
A5	1/7	1/4	1/3	1	1/5	1/3	0.0395
C2	1/2	2	3	5	1	4	0.2462
C3	1/5	1/3	1/2	3	1/4	1	0.0716

,

Table 8. Weight of secondary indicators of one-dimensional quality.

	A4	A6	B4	C1	C4	D1	D4	E2	Weight
A4	1	6	2	1/2	1/3	3	4	5	0.1591
A6	1/6	1	1/5	1/6	1/8	1/4	1/3	1/2	0.0249
B4	1/2	5	1	1/3	1/4	2	3	4	0.1094
C1	2	6	3	1	1/2	4	5	5	0.2217
C4	3	8	4	2	1	5	5	6	0.3184
D1	1/3	4	1/2	1/4	1/5	1	2	3	0.0747
D4	1/4	3	1/3	1/5	1/5	1/2	1	3	0.0566
E2	1/5	2	1/4	1/5	1/6	1/3	1/3	1	0.0353

and

Table 9. Weight of secondary indicators of attractive quality.

	B1	B2	C5	D2	D3	E1	Weight
B1	1	2	1/2	1/3	1/5	3	0.0980
B2	1/2	1	1/3	1/4	1/5	2	0.0651
C5	2	3	1	1/2	1/3	4	0.1583
D2	3	4	2	1	1/2	5	0.2456
D3	5	5	3	2	1	6	0.3897
E1	1/3	1/2	1/4	1/5	1/6	1	0.0432

.

Next, we examine whether it meets the consistency test criteria. If CR < 0.1, this indicates that the consistency test is satisfied. The smaller the CR, the better the consistency of the judgment matrix. All weight values are displayed in Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9. Based on the previously mentioned calculation formula, we find that all consistency ratios (CRs) are less than 0.1, satisfying the consistency requirement. The specific calculation results are as follows (see

Table 10. Summary of consistency test results.

	λmax	CI	RI	CR
First-level quality indicator	4.0818	0.0273	0.89	0.0306
Secondary indicators of must-be quality	6.1607	0.0321	1.26	0.0255
Secondary indicators of one-dimensional quality	8.3975	0.0568	1.41	0.0403
Secondary indicators of attractive quality	6.1338	0.0268	1.26	0.0212

), Additionally, the ranking of all indicators is presented in

Table 11. Summary of all indicators weight results.

Dimension	Weight	Index	Weight	Comprehensive Weight	Sequence
Must-be quality	0.4564	A1	0.3837	0.1751	1
	0.4564	A2	0.1582	0.0722	4
	0.4564	A3	0.1008	0.0460	8
	0.4564	A5	0.0395	0.0180	14
	0.4564	C2	0.2462	0.1124	2
	0.4564	C3	0.0716	0.0327	11
One-dimensional quality	0.3032	A4	0.1591	0.0482	7
	0.3032	A6	0.0249	0.0076	19
	0.3032	B4	0.1094	0.0332	10
	0.3032	C1	0.2217	0.0672	5
	0.3032	C4	0.3184	0.0965	3
	0.3032	D1	0.0747	0.0226	13
	0.3032	D4	0.0566	0.0172	15
	0.3032	E2	0.0353	0.0107	17
Attractive quality	0.1461	B1	0.098	0.0143	16
	0.1461	B2	0.0651	0.0095	18
	0.1461	C5	0.1583	0.0231	12
	0.1461	D2	0.2456	0.0359	9
	0.1461	D3	0.3897	0.0569	6
	0.1461	E1	0.0432	0.0063	20

:

In a comprehensive sense, in this study, through the classification (see Table 3) and ranking (see

Table 12. The results of sequence.

Classification Item	Sequence
	Must-be quality > One-dimensional quality > Attractive quality > Indifferent quality
Must-be quality	A1 > C2 > A2 > A3 > C3 > A5
One-dimensional quality	C4 > C1 > A4 > B4 > D1 > D4 > E2 > A6
Attractive quality	D3 > D2 > C5 > B1 > B2 > E1
Experience and Accessibility	A1 > A2 > A4 > A3 > A5 > A6
Orientation and Information Support	B4 > B1 > B2
Safety and Emergency Assurance	C2 > C4 > C1 > C3 > C5
Alternative Paths and Flexible Space	D3 > D2 > D1 > D4
Cultural Identity and Ecological Co-construction	E2 > E1

) of tourist demands using KANO and AHP, we obtained the following results:

According to the analysis results, we found that must-be qualities are the most important, as they are the basic demands that tourists think urban forest parks must fulfil. From the weight ranking, the order is high forest cover (A1), emergency call facilities and medical teams (C2), a comfortable environment (A2), convenient transportation (A3), an evacuation guidance system (C3), and the reasonable setting of public facilities (A5). Next are the one-dimensional qualities, with importance ranked as follows: communication support (C4), emergency shelters and supplies (C1), reasonable route layout (A4), risk warning reminders (B4), trail connectivity (D1), backup forest buffer (D4), photo-worthy spot (E2), and parent–child activity facilities (A6). Finally, attractive qualities are ranked as follows: backup service points (D3), alternative routes (D2), security patrol mechanism (C5), multilingual and clear guide signs (B1), intelligent guide terminals (B2), and regional culture (E1).

In addition, in the experience and accessibility dimension, four items were classified as must-be qualities, among which high forest cover (A1) was considered the most important by tourists, followed by emergency call facilities and medical teams (C2), a comfortable environment (A2), convenient transportation (A3), an evacuation guidance system (C3), and the reasonable setting of public facilities (A5). These are essential qualities for urban forest parks. In addition, a reasonable route layout (A4) and parent–child activity facilities (A6) were classified as attractive qualities, very beneficial for improving tourist loyalty to the park.

In the orientation and information support dimension, risk warning reminders (B4) were classified as one-dimensional qualities. Except for route classification guidelines (B3), which was an indifferent quality, multilingual and clear guide signs (B1) and intelligent guide terminals (B2) were both classified as attractive qualities.

In the safety and emergency assurance dimension, emergency call facilities and medical teams (C2) and an evacuation guidance system (C3) are must-be qualities. Next is emergency shelters and supplies (C1) and communication support (C4), which are one-dimensional qualities. When faced with external risks, we can clearly feel the importance of emergency call facilities, medical teams, and evacuation guidance systems to tourists. Finally, security patrol mechanisms (C5) were deemed attractive qualities.

In the dimension of cultural identity and ecological co-construction and photo-worthy spots (E2) were one-dimensional qualities. If this quality is enhanced, the competitiveness of the forest park will also be significantly strengthened. Regional culture (E1) was an attractive quality. We find that tourists show a desire to pursue local characteristics and culture, hoping to obtain something beyond material pursuits. In addition, eco-volunteer programs (E3) and environmental friendliness (E4) are considered to be indifferent qualities.

4. Discussion

Through semi-structured interviews and KANO questionnaire data, this study identified and classified tourist demands into four categories: must-be qualities, one-dimensional qualities, attractive qualities, and indifferent qualities. At the same time, AHP was used to rank the importance of each dimension and its demand indicators. These results not only help park managers and designers provide targeted optimization directions and measures in the management and design process to improve tourist satisfaction, but also provide support for promoting the sustainable development of urban tourism resilience.

The results show that the high forest coverage rate is the basic demand that tourists consider most important to be met. It is not only a key indicator that affects tourists’ perception of nature but also an important parameter for measuring the ecosystem services of urban green spaces [77]. Previous studies have confirmed that high forest coverage can improve urban thermal comfort, air quality, and visual aesthetics [27] and have positive effects on physical and mental health [78,79,80]. Especially after COVID-19, the public’s preference for healthy and ecological tourism has grown, and the quality of the forest ecological environment has become an important part of tourism appeal [81]. In addition, indicators such as a comfortable environment, convenient transportation, and reasonable setting of public facilities are also considered by tourists to be the “minimum requirements” for urban forest parks. Similarly to other studies [21,51], this study also confirmed that the improvement of these services not only improves tourist accessibility but also significantly reduces the time and physical costs of travel, satisfying tourists’ emphasis on efficiency while enhancing the sustainable attractiveness of tourism [79]. This requires park managers and designers to ensure comprehensive forest coverage, infrastructure, and emergency systems while prioritizing circulation organization and nodes in their designs. By establishing a clear, efficient path network, the fluidity of the spatial experience is enhanced.

In addition, we have identified other potential demands with significant attractions, especially parent–child activity facilities and photo-worthy spots, which have a positive effect on enhancing tourist engagement and emotional attachment. In recent years, the parent–child tourism market has shown a rapid growth trend. Especially under the impetus of the national “three-child” policy, the demand for parent–child and family-oriented spaces has become an important direction for tourism product design [82,83]. At the same time, with the popularity of social media, tourism is no longer just about enjoying the scenery but also a process of generating and sharing content. Photo-worthy spots have become an important trigger for young tourists to record, display, and disseminate their travel experiences [84]. According to data collected in this study, about 35% of young people prefer urban forest parks. Therefore, in the design of forest park spaces, photo-worthy spots that integrate landscape aesthetics, cultural imagery, and interactive participation functions not only help expand communication but also stimulate emotional resonance and the desire to revisit.

Most importantly, the study further found that when faced with risky situations, tourists’ demands no longer stop at the level of leisure and entertainment. Emergency call facilities, medical teams, and evacuation guidance systems are now considered must-have qualities, reflecting tourists’ high level of concern for emergency response when visiting urban forests. This is especially true after the pandemic, as tourists have become much more aware of the importance of preventing external risks. This is consistent with the research results proposed by Dong et al. (2024) [85]. Therefore, at the design level, building a comprehensive infrastructure system and an efficient emergency response mechanism has become the core path to improving the resilience of urban forest tourism. At the same time, communication support, emergency shelters and supplies, trail connectivity, and backup forest buffers are classified as one-dimensional qualities. Although their absence will not directly lead to a decline in satisfaction, if they are met, they will greatly enhance the overall experience and satisfaction of tourists. The improvement of these services will help enhance the competitiveness of Xishan Forest Park. The improvement of such facilities can greatly alleviate tourists’ concerns about potential emergencies, especially when tourists are on long or intense trips. This provides direction for managers to formulate corresponding strategies.

In addition, alternative routes, backup service points, and security patrol mechanisms play a key role in improving the tourist experience and enhancing loyalty [86]. Resilient spaces and alternative facilities greatly alleviate tourists’ concerns when encountering uncertain dangers. Therefore, designers should avoid creating single paths during the design process. While ensuring that the main route is clear, they should set up several auxiliary routes. These paths can provide exploratory options during normal times and can be quickly converted into emergency evacuation routes in the event of sudden closures, effectively enhancing spatial resilience. Additionally, a routine maintenance and patrol mechanism should be established. This will significantly reduce response times, enhance incident handling efficiency, minimize the negative impact of accidents, and boost tourist safety and trust while greatly increasing tourist loyalty.

5. Conclusions

This study uses the KANO model in combination with AHP to classify and rank tourist demands, identify factors affecting the resilience of Xishan Forest Park tourism, emphasize the diverse demand structure of tourists in normal and high-risk situations, strengthen the market competitiveness of urban forest parks, and provide a new perspective on improving the resilience of Kunming’s urban tourism. The results show that tourists’ core concerns not only include the ecological environment and infrastructure services but also significant demands for communication support, emergency facilities, path redundancy, and spatial flexibility when facing uncertain risks. This finding reflects that contemporary tourists’ comprehensive experience demands for “security–control–resilience” constitute a new direction for the design or resilient urban tourism.

According to our results, first priority should be given to ensuring the most basic must-be qualities for tourists, such as high forest coverage, a comfortable environment, convenient transportation, and a comprehensive medical emergency system, which are the foundation of tourist satisfaction. Second, safety and emergency measures corresponding to one-dimensional quality should be strengthened to enhance the risk response capabilities of forest spaces. In addition, the introduction of attractive elements such as parent–child facilities, photo-worthy spots, and smart guides can further expand the customer base of urban forest parks, enhance spatial participation and emotional connection, and strengthen tourist loyalty and communication.

It should be noted that the geographical and cultural characteristics of Xishan Forest Park directly influence the categorization and prioritization of tourist demands. As a typical urban mountain forest with complex terrain and diverse pathways, the park elicits heightened sensitivity among tourists toward trail connectivity and wayfinding. The presence of multiple religious and commemorative sites—such as the Longmen Grottoes and Huating Temple—reinforces tourists’ demand for cultural interpretation. Simultaneously, middle-aged and elderly individuals form the primary tourist demographic, with correspondingly greater emphasis on facilities and safety considerations.

In addition, this study has certain significance. On the one hand, it introduces the KANO-AHP fusion analysis framework to systematically identify different types of demands and their priorities, providing a data basis for improving the resilience of space design. On the other hand, the study starts from the perspective of tourists and provides urban forest park managers and designers with highly operable optimization strategies for actual planning, as well as theoretical support and experience references for promoting the sustainable development of Kunming’s urban tourism resilience.

Of course, this study still has certain limitations, and a full understanding of these limitations will help to comprehensively understand the research results and provide direction for follow-up research. First, the scope of the study mainly focused on Xishan Forest Park in Kunming, and the results obtained may not be sufficient to cover the characteristics of other urban forest attractions and may not be fully applicable to forest parks in other regions. In the future, the universality of the conclusions can be verified through cross-regional comparative studies. Second, the five dimensions and 23 indicators identified in this study also have certain regional and time-sensitive characteristics. Even for Xishan Forest Park itself, it is necessary to further update the dynamic tracking of demand. In the future, behavioral experiments, a mobile trajectory, or physiological perception data can be introduced to obtain more timely and accurate data.