Construction and empirical study of China's snow sports evaluation Index system

Introduction: This study aimed to construct a scientifically-grounded evaluation index system for snow sports development in China, addressing persistent challenges in mass participation, industry cultivation, and regional coordination despite competitive achievements. The research was motivated by the transformative impact of the 2022 Beijing Winter Olympics and the need for a locally adapted evaluation framework that maintains international comparability.

Methods: A mixed-methods approach was employed, combining in-depth interviews with 43 experts analyzed via grounded theory to develop a three-level indicator system (3 primary, 13 secondary, and 54 tertiary indicators). A combined weighting method integrated objective data characteristics and normative design principles, applied to panel data from 2015–2019. Regression analysis was conducted to quantify the impact of Winter Olympics investment.

Results: The overall development index increased 10.92-fold from 2015 to 2019 with mass participation showing the most rapid growth (12.71-fold) followed by competitive development (10.64-fold) and economic development (8.82-fold). Regression analysis confirmed that Winter Olympics investment had a significant positive impact (β= 0.1661, p < 0.1) while cost expenditure was a significant barrier (β= −0.0190 to −0.0398, p < 0.05).

Discussion: Strategic Olympic investment catalyzed holistic development, particularly in mass participation, amplified by policy campaigns like “Engaging 300 Million People in Ice and Snow Sports.” The concentric circle model validated the interconnectedness of competitive, mass, and economic dimensions. However, high operational costs threaten long-term sustainability, necessitating policies to improve.

1 Introduction

The sustainable development of snow sports is intricately linked to natural environments, cultural traditions, and economic factors. Since China's debut at the 1980 Lake Placid Winter Olympics (1), the country's snow sports sector has undergone significant evolution over four decades. While notable competitive milestones have been achieved—exemplified by China's first Olympic gold medal in snow events at Beijing 2022—persistent challenges remain in mass participation, industry cultivation, and regional coordination (2). The successful bid for the 2022 Beijing Winter Olympics served as a transformative catalyst (3), prompting national strategic initiatives such as the “Engaging 300 Million People in Ice and Snow Sports” campaign (2018–2022) and the 14th Five-Year Sports Development Plan. These policies established a development model characterized by “competitive leadership–mass popularization–industrial synergy” (4), creating a practical foundation for sustainable growth in the post-Olympic era.

Globally, the assessment of mega-sporting event legacies provides valuable frameworks for evaluation. The International Olympic Committee's (IOC) Olympic Games Impact (OGI) study and subsequent legacy assessment methodologies emphasize the multidimensional economic, social, and environmental effects of such events (5). Comparative analyses of Vancouver 2010, Sochi 2014, and PyeongChang 2018 reveal a consistent structural pattern involving the interplay between “core competition,” “public participation,” and “industrial and environmental spillover” (6), which aligns with the conceptual approach adopted in this study. However, the Chinese context presents unique characteristics, including simultaneous phases of rapid infrastructure expansion and intensive policy support, set against a backdrop of considerable climatic and geographical diversity (7, 8). This necessitates a locally adapted evaluation framework that maintains international comparability while incorporating context-specific dimensions related to facilities and organizational structures.

Despite these advancements, a significant research gap persists in the systematic development of a comprehensive evaluation index system for snow sports in China (9). Existing studies often suffer from excessive subjectivity in indicator selection and weight distribution, lacking unified standards that would enable cross-validation and comparison of research outcomes (10, 11). Furthermore, scholarly work has insufficiently addressed the empirical validation of such index systems through practical application and robust quantitative analysis (12, 13). These limitations not only constrain the practical utility of existing frameworks but also impede evidence-based policymaking and long-term strategic planning for the sector's development (14, 15).

To address these research deficiencies, this study employs a mixed-methods approach combining qualitative and quantitative analysis. The research is structured around several key objectives: (1) to develop a scientifically grounded evaluation index system for China's snow sports development through rigorous methodological procedures; (2) to establish appropriate weighting mechanisms that balance objective data characteristics and normative design principles; (3) to empirically validate the proposed framework through longitudinal analysis of development trends between 2015 and 2019; and (4) to quantify the impact of Winter Olympics investment on different dimensions of snow sports development. By addressing these objectives, this research aims to provide both theoretical contributions to sports development evaluation and practical insights for policymakers and industry stakeholders navigating the post-Olympic development landscape.

2 Methods

2.1 Expert interview

The interview method can be categorized into structured, unstructured, and semi-structured types based on the degree of control over the interview process (16). This study opted for a combination of unstructured and semi-structured interviews to collect data. The primary reason for this choice was the researchers’ initial uncertainty about which factors might reflect the impact of the 2022 Beijing Winter Olympics on the development of snow sports (17). Consequently, an entirely open attitude was adopted during the initial interviews. As the research progressed, the approach gradually transitioned from unstructured to semi-structured, leading to the development of a semi-structured interview topic outline (Table 1). All interviews were conducted in strict adherence to standard ethical research principles. Prior to participation, each expert was fully informed about the study's purpose and provided verbal consent. To ensure confidentiality and promote candid responses, complete anonymity was guaranteed to all participants; therefore, all data were collected and analyzed without recording any personally identifiable information.

Table 1

Table 1. Semi-structured interview outline.

Currently, qualitative research often employs purposive sampling in interviews, selecting research subjects that can provide rich information based on the research objectives. This method, also known as “theoretical sampling,” aims to capture the internal experiences of the subjects until data saturation is achieved.Unlike probabilistic sampling in quantitative research, it places less emphasis on the number of respondents. In this study, the selection of interview subjects needed to consider the breadth and depth of their understanding of the development of snow sports in China, observing from different perspectives and possessing deep insight. Therefore, based on the “information overload” theory (18), this study selected top experts in the domestic skiing field, using snowball sampling. From March 27 to November 3, 2018, a total of 43 people were interviewed through multiple real-time communication channels such as phone calls, WeChat video, and QQ voice (Table 2).

Table 2

Table 2. Overview of interviewee profiles for snow sports study.

To efficiently retrieve and analyze interview data, it is stored in coded form (19). The code structure is as follows: the first two digits represent a sequential code, e.g., 17 denotes the 17th interviewee; the third digit represents the interview type, with M indicating an in-person interview and N indicating a non-in-person interview; the last four digits represent the interview date, e.g., 1119 represents November 19, 2018.

To ensure a comprehensive and multi-perspective understanding of China's snow sports development since the successful Olympic bid, a combination of purposive sampling and snowball sampling was employed. Inclusion criteria were: (1) direct involvement in or close observation of key sectors of snow sports (e.g., elite training and administration, recreational ski operations, resort facilities and equipment firms, events and education/training, industry associations and relevant government units); (2) demonstrated breadth and depth of insight through substantial professional experience and/or research; and (3) coverage across major snow sports regions and business types in China. Individuals unable to provide verifiable first-hand practice information or research experience were excluded from the study. To mitigate homogeneity risks inherent in snowballing, referrals were proactively supplemented with experts from different regions and roles (e.g., Northeast–North–Northwest–East–Southwest; state-owned vs. private; venues vs. equipment; education vs. events), while negative case analysis and heterogeneity comparison were employed during coding. Data saturation followed an operational rule of “no new first-level concepts/codes emerging across two consecutive interviews,” documented via MAXQDA2018 memos; after reaching saturation, additional interviews were conducted to confirm saturation stability.

2.2 Grounded theory

Scholars such as Hu Jiao (20), Guan Jiang (21), and Zhao De (22), through empirical analysis, have concluded that using the qualitative research method of grounded theory to attribute research problems is a feasible research approach. Grounding is the process of attribution, an effective qualitative method for identifying influencing factors for research problems. The combination of grounding and attribution is significant for enhancing the accuracy and scientific nature of social science research (23). This study combines grounded theory with attribution theory, starting from the collection of individual experience fragments, gradually summarizing the various indicators of the development of snow sports in China, and strives to provide new insights into the study of China's snow sports development through more scientific and rigorous means and methods.

The grounded theory research method, proposed by sociologist B.G. Glaser and others, is a qualitative research method that provides a systematic procedure for analyzing large amounts of qualitative data and forming the theoretical basis of research (24). In qualitative research, scholars adopt different data organization and analysis strategies based on research habits, as introduced in various qualitative research methods in “The SAGE Handbook of Qualitative Research (25). "Professor Chen Xiangming proposed the linear and interactive modes of data analysis, believing that the two can be organically unified (26). Therefore, this study adopts Professor Chen Xiangming's viewpoint, using the grounded theory research method to construct a theoretical framework through bottom-up concept extraction and comparison, and conducting clustering and modification until theoretical saturation is achieved.The specific operations include open coding, axial coding, selective coding, and theory formation.

In this study, MAXQDA 2018 was used for data management and coding, and the implementation was carried out in three progressive stages: “open—main axis—selective”. An initial codebook was established based on several interview texts. The operational definitions were refined through multiple rounds of iterations, and the code evolution and decision-making logic were recorded in memos. To enhance credibility, some of the interview texts were independently reviewed and compared, differences were discussed and resolved, and the codebook and typical anchor attributive paragraphs were subsequently updated. Subsequently, consistent encoding is performed on the remaining texts, and regular comparisons and negative case searches are continuously conducted to ensure logical consistency and conceptual saturation from the data to the categories and main categories.

Coding illustration (excerpt → concept → category → tertiary indicator): Example A (facility expansion): Excerpt—“..upgraded cable cars and chairlifts; added snowmaking machines and snow groomers; invested nearly 300 million RMB..” (05M0415). Open codes: lift upgrades; new snowmaking; new grooming; capital input. Axial category: facility capacity expansion (lifts/magic carpets/snowmaking/grooming). Selective mapping to tertiary indicators (Table 3): “number of aerial lifts/detachable lifts,” “number/length of new and operating magic carpets,” “number of new imported/domestic snowmaking machines,” “number of new imported/domestic (second-hand) snow groomers.”Example B (competitive services and talent): Excerpt—“..assembled outstanding coaches and technical staff; adopted a four-year team leader responsibility system..” (11M0530). Open codes: coaches/technical staff; cross-disciplinary training team; leader responsibility. Axial category: competitive service system and talent development. Tertiary indicators: “number of competitive service personnel,” “number of national team athletes,” and “number of research projects in competitive snow sports.”

Table 3

Table 3. Examples of open coding from interview transcripts.

2.2.1 Open coding (level one)

Open coding is the initial stage of the grounded theory analysis method, aiming to find localized concepts and understand the way researchers view the world through data fragmentation, concept assignment, and recombination (27). This process usually occurs simultaneously with data entry, extracting concepts and categories from textual data.This study uses MAXQDA2018 qualitative research software to analyze 43 interview materials, with Table 3 showing some of the text coding.

Through the analysis of 43 interview materials, using the Beijing Winter Olympics’ impact on the development of snow sports as a clue and following the principles of open coding, a total of 184 concepts were extracted from the raw data (Table 4).

Table 4

Table 4. Key concepts extracted from winter olympics impact on snow sports.

2.2.2 Axial coding (secondary level)

Axial coding primarily involves further clustering the concepts obtained from the initial coding, establishing connections between categories based on relationships such as similarity, process, and subject.In this study, the concepts and their inherent and logical relationships were continuously compared and classified by referencing the model established by semantic segmentation scholar Wang Zhijin (28) (Figure 1).

Figure 1

Figure 1. Framework of semantic relationships in grounded theory analysis.

By carefully analyzing and categorizing the 184 concepts obtained through open coding, 38 categories were ultimately formed, which serve as the primary indicators of the development of snow sports (Table 5).

Table 5

Table 5. Categorized impacts of Beijing winter olympics on snow sports development.

2.2.3 Selective coding (tertiary level)

Selective coding refers to the process of choosing a core category and systematically linking all other categories to this core category (Strauss & Corbin) (29). This stage of the coding process integrates all previous analytical work, analyzing the associations between the core or main category and other categories.Data is grouped to facilitate the identification of patterns and trends when observing specific dimensions within the categories.In this study, continuous comparison was conducted using memos and original data, with “development of snow sports” as the core category. All main categories and categories were integrated to develop the theoretical model as shown in Figure 2.

Figure 2

Figure 2. Concentric circle model of snow sports development indicators.

In constructing the development indicator system for snow sports, this study reclassified the indicators, considering the potential overlaps and intersections in the traditional fourfold classification of sports in China (competitive sports, mass sports, school sports, and sports industry) in practical application. Given the unique nature of snow sports, school sports are regarded as part of mass sports, while the sports industry and mass sports overlap in practical work. Therefore, this study divides the indicators into three aspects: “competitive development indicators for snow sports,” “mass development indicators for snow sports,” and “economic development indicators for snow sports.”

The logical relationship of the snow sports development indicators forms a “conical” structure in three-dimensional space, appearing as a “pyramid” shape from the front and side views. Competitive sports are at the “pinnacle,” representing the highest level of snow sports in China; mass sports form the “body” of the pyramid, supporting the height of competitive sports through widespread participation; the development of the sports industry is at the “base,” providing economic support. From a top view, the indicator system presents a “concentric circle” layered pattern, with competitive sports development indicators at the core, mass sports development indicators in the middle layer, and economic development indicators on the periphery.

This logical relationship indicates that the development of snow sports requires the leadership of competitive sports, the popularization of mass sports, and the economic support of the sports industry. The level of development in competitive sports directly reflects the competitive level of a project, the degree of popularization in mass sports indicates the sustainability and social impact of a project, and the development of the sports industry provides the necessary economic foundation and resource assurance for snow sports.

The integration pathway is structured as follows: semi-structured interviews are first conducted to elicit first-hand experiential factors; open, axial, and selective coding based on grounded theory is then applied to derive a three-level indicator system; combined weighting methods are used to obtain quantifiable weights; multi-source data from 2015 to 2019 are employed to compute indicators and synthesize development indices; panel fixed-effects and random-effects regressions are performed to test external validity and impact mechanisms; finally, empirical results provide feedback to refine indicator interpretations and weight explanations, forming a closed methodological loop.

2.3 Index system construction

Based on attribution theory and employing a research paradigm that combines interviews and grounded theory, this study interviewed leading experts and scholars in the field of snow sports in China to obtain firsthand information on the development of snow sports since the bid for the 2022 Beijing Winter Olympics. Through coding analysis of this data, an evaluation indicator system for the development of snow sports in China was established, consisting of 54 tertiary indicators, 13 secondary indicators, and 3 primary indicators (Table 6).

Table 6

Table 6. Structure of snow sports evaluation indicator system.

2.4 Data sources

Currently, the statistical data on snow sports in China is still insufficient. Therefore, to enhance related research, this study collects snow sports data from multiple sources. Since the data released at the end of 2021 pertains to 2020, and some data collected at the end of the year has not yet been published, this study selects the time span of 2015–2019 for relevant data. Data sources include the “China Ski Industry Core Data Report,” the “Beijing 2022 Winter Olympics Bid Report,” Qichacha, and some data obtained through surveys and interviews.

The data from 2015 to 2019 primarily derive from the following sources: (1) The “Core Data Report on China's Ski Industry” and related annual industry reports, which provide information on facility-related indicators such as the number of ski resorts, cable cars, magic carpets, snowmaking and grooming equipment, as well as indoor and dry ski facilities; (2) The “Beijing 2022 Winter Olympics Bid Report” and publicly available preparatory documents, which serve as references for investment standards associated with the event and its infrastructure development; (3) Enterprise registration and business credit databases, including Qichacha, which are used to obtain data on registered capital and employee numbers of skiing-related enterprises; (4) Estimates derived from industry research and survey questionnaires, with consistent methodological calibration applied across time periods. Potential limitations include discrepancies in timing or definitions across sources, underreporting by small and medium-sized enterprises, and regional variations in statistical practices. These issues have been mitigated through cross-verification using multiple data sources, year-on-year trend analysis, and outlier identification via longitudinal tracking.

2.5 Combined weighting method determination

The combined weighting approach integrated both objective data characteristics and subjective design considerations (30). The coefficient of variation method was first employed as an objective weighting method, calculated as follows:

$\mathrm{Vi}=\sigma \mathrm{i}/\mu (1)$

$\mathrm{Wi}=\mathrm{Vi}/\mathrm{\Sigma }\mathrm{Vi}(2)$

where μ represents the mean of the indicator data, σ denotes the standard deviation, Vi denotes the coefficient of variation, and Wi represents the normalized weight. Subsequently, equal weighting was applied to reflect the normative design principle of equal dimensional importance (Table 7). The final combined weight was determined as the arithmetic mean of the weights derived from both the coefficient of variation method and the equal weighting method, as detailed in Table 8.

Table 7

Table 7. Combined weights for tertiary indicators in snow sports evaluation.

Table 8

Table 8. Hierarchical weight system for snow sports development indicators.

2.6 Calculation and evaluation

The development index of snow sports in China was calculated using the aforementioned methods, and the specific results are shown in Table 9. As shown in Table 9 and Figure 3, the development of snow sports in China has been rapid. From 2015 to 2019, the overall snow sports development index rose from 0.0705 to 0.7704 (10.92 times). The competitive development index increased from 0.0745 to 0.7129 (10.64 times), the mass (public) development index from 0.0576 to 0.7323 (12.71 times), and the economic development index from 0.0869 to 0.7160 (8.82 times). These results suggest substantial progress across all dimensions, with the fastest growth observed in mass participation. However, due to geographical and climatic factors, the economic benefits of snow sports have increased relatively slowly, especially in tropical and subtropical provinces, where high development costs have prevented full realization of economic effects. Nonetheless, the overall development speed of snow sports in China remains highly remarkable.

Table 9

Table 9. Development Index of snow sports and Its dimensions in China, 2015–2019.

Figure 3

Figure 3. Development of snow sports in China from 2015 to 2019.

3 Results

3.1 Variable selection

This study primarily investigates the impact of the Beijing Winter Olympics on the development of snow sports in China. Therefore, the dependent variable is the development level of snow sports in China, represented by a snow sports development index; the key independent variable is the level of investment in the Beijing Winter Olympics (Input). The investment in the Beijing Winter Olympics encompasses various aspects, many of which are intangible assets that cannot be specifically quantified. Therefore, the primary core investment—economic investment—is used as a substitute variable for the level of investment in the Beijing Winter Olympics.

The control variables selected are economic development level (Economy), degree of openness (Open), level of fiscal expenditure (Finance), and cost expenditure (Cost). The reasons for variable selection are as follows: (1) The level of economic development is the foundation for high-quality development of a country and region, and it plays a crucial supporting role in the development of snow sports.This is not only reflected in the construction of snow sports infrastructure but also in the increased consumer willingness to participate in snow sports as their purchasing power grows. Therefore, the level of economic development significantly impacts the growth of snow sports (31). (2) Regions with advanced snow sports development are primarily in Europe and North America. Further expansion of openness will facilitate greater interaction between China's snow sports sector and the international community, potentially leading to a more extensive snow sports industry trade chain and having a positive impact on snow sports development (32). (3) The level of fiscal expenditure determines the development progress of snow sports infrastructure, which is critically linked to the development of snow sports and the broader ice and snow sports sector (33). (4) The long-term and efficient development of snow sports largely depends on cost issues. Currently, high costs are one of the primary barriers to the development of snow sports. For businesses operating in snow sports, achieving sustained profitability is a prerequisite for long-term operations. Excessive operating costs may result in prolonged losses for companies (34), and if government subsidies are withdrawn, the entire industry could face significant operational challenges. Therefore, cost expenditure is a critical factor affecting the development of snow sports (35).

3.2 Model specification

Based on the multidimensional panel data of snow sports development, this study employs a fixed effects model for estimation, as specified in Equation 3 (36, 37). In this model, Snowsport i, t represents the level of snow sports development, Input i, t denotes the level of investment in the Winter Olympics, Control i, t indicates the control variables, μ i represents the individual effects, σt denotes the time effects, and ε it represents the random disturbance term. Subsequently, to verify the stability of the empirical results, this study will conduct robustness checks to ensure the reasonableness and scientific validity of the results.

$\mathrm{Snowsporti},\mathrm{t}=\alpha +\beta 1\mathrm{Inputi},\mathrm{t}+\mathrm{\Sigma }\beta \mathrm{iControli},\mathrm{t}+\mu \mathrm{i}+\sigma \mathrm{t}+\epsilon \mathrm{it}(3)$

3.3 Empirical outcomes

The overall empirical test results of the impact of Winter Olympics investment on the development of snow sports are shown in Table 10. The regression coefficient for Input is 0.1661, which is significantly positive at the 10% level; the regression coefficient for Economy is 0.1593, which is not significant; the regression coefficient for Open is 0.0555, which is significant at the 10% level; the regression coefficient for Finance is 0.0197, which is not significant; the regression coefficient for Cost is −0.0190, which is significantly negative at the 5% level; the regression coefficient for the constant term is −1.8988, which is significant at the 1% level, with a goodness-of-fit of 0.7402. From the regression results, it can be concluded that the model fits well, and the investment in the Winter Olympics has a significant positive impact on the development of snow sports, enhancing its development level. Among the control variables, the level of economic development has a limited direct promoting effect on the development of snow sports. In contrast, the level of openness significantly enhances the competitive level of snow sports, possibly due to increased international cooperation opportunities. Although fiscal expenditure promotes the development of snow sports, its effect is not significant, requiring more policy guidance and industry support. Conversely, cost expenditure significantly hinders the development of snow sports, as high costs increase operational pressure on enterprises, adversely affecting the industry (38, 39).

Table 10

Table 10. Regression analysis of winter olympics investment impact on snow sports.

In the robustness checks, this study employed a Random Effects model (RE) and conducted a Fixed Effects regression (FE) using the transformed variable snowsport1. The regression results are shown in columns (2) and (3) of Table 10. In the RE model, the regression coefficient for Input is 0.1859, significantly positive at the 10% level; the coefficient for Economy is 0.1896, not significant; the coefficient for Open is 0.0859, significantly positive at the 10% level; the coefficient for Finance is 0.0347, not significant; the coefficient for Cost is −0.0398, significantly negative at the 5% level; the constant term coefficient is −2.5984, significantly negative at the 1% level, with a goodness-of-fit of 0.7052.In the FE (snowsport1) model, the regression coefficient for Input is 0.1752, significantly positive at the 10% level; the coefficient for Economy is 0.1655, not significant; the coefficient for Open is 0.0714, significantly positive at the 10% level; the coefficient for Finance is 0.0254, not significant; the coefficient for Cost is −0.0259, significantly negative at the 5% level; the constant term coefficient is −3.5587, significantly negative at the 1% level, with a goodness-of-fit of 0.6539. From the RE and FE (snowsport1) regression results, it is evident that the model fits well, and the core explanatory variable, Winter Olympics investment, has a significant promoting effect on the development of snow sports. The consistency with the basic regression model results indicates that the empirical conclusions are robust (40).

Marginal significance (approximately at the 10% level), as observed in variables such as Input and Open, is interpreted as offering preliminary statistical support for directional effects and trends. However, conclusions should be drawn cautiously to avoid overinterpretation, particularly given the heightened risk of Type I errors in smaller samples. Building on the robustness checks detailed above, the consistent direction and magnitude of effects across fixed effects (FE), random effects (RE), and variable-transformed models strengthen the credibility of the findings. Furthermore, the inclusion of individual effects (μi) and time effects (σt) helps control for unobserved heterogeneity and mitigate potential biases from contemporaneous shocks. Potential confounders, such as regional climate conditions, macro-tourism cycles, and demographic shifts, may influence results but were not fully incorporated due to data limitations, potentially affecting causal identification (39). Future research could address these by expanding data sources and refining controls for greater precision (41, 42).

From a policy perspective, statistical significance at the 10% level is interpreted as providing directional evidence suitable for pilot programs, phased evaluations, and iterative policy calibration, rather than as a basis for one-off, large-scale resource reallocations. Consistency across RE and transformed-FE models suggests a robust positive effect of Olympic investment (43, 44). Based on these findings, it is recommended to prioritize finely tuned measures that increase openness and reduce costs, implemented incrementally with rapid evaluation cycles to minimize policy risks (45).

4 Discussion

The empirical findings of this study, derived from a systematically constructed evaluation framework, reveal a period of unprecedented growth in China's snow sports sector following the successful bid for the 2022 Beijing Winter Olympics. The overall development index surged by a factor of 10.92 between 2015 and 2019, with mass participation experiencing the most dramatic expansion (46). This pattern of growth, where public engagement outpaces competitive and economic dimensions, is a phenomenon observed in other contexts of mega-sporting events. The “inspirational effect” or “demonstration effect” of the Olympics, where elite athletic excellence motivates broader public participation, has been documented in previous research (47). In the Chinese context, this effect was significantly amplified and accelerated by the top-down, state-led “300 Million People in Ice and Snow Sports” campaign, creating a unique synergy between Olympic inspiration and coordinated policy implementation that may explain the exceptionally rapid growth in mass participation compared to other Winter Olympic hosts (48).

The concentric circle model developed through this research provides a robust theoretical framework for understanding the hierarchical and interconnected nature of snow sports development. This model aligns with international mega-event legacy frameworks that identify the interplay between core competition, public participation, and industrial spillover (49). However, it extends these models by incorporating the distinctive characteristics of the Chinese development pathway, which involved simultaneous investment across all three dimensions within a compressed timeframe. The empirical validation of this model through longitudinal data adds a significant contribution to the theoretical discourse on sport development, offering a replicable framework for other nations seeking to leverage mega-events for holistic sectoral growth.

The regression analysis quantifies the significant positive impact of Winter Olympics investment (β = 0.1661, p < 0.1), affirming the catalytic role of strategic mega-event investment. This finding is consistent with studies on previous Winter Games, which have shown that targeted investment can serve as a powerful driver for sport infrastructure and participation. However, the concurrent identification of cost expenditure as a significant barrier (β = −0.0190 to −0.0398, p < 0.05) highlights a critical challenge for long-term sustainability. High operational costs can erode the economic viability of resorts and facilities, particularly in regions without natural advantages, potentially threatening the longevity of the participation legacy once the immediate post-Olympic period concludes. This underscores the necessity of complementing initial investment with policies aimed at improving operational efficiency and reducing barriers to entry for both operators and participants.

While the growth trajectory is impressive, the relatively slower pace of economic development (8.82-fold increase) compared to mass and competitive growth suggests a lag in converting participation into sustainable economic value. This may reflect the initial phase of development being dominated by infrastructure investment and capacity building, with the maturation of a robust sports industry ecosystem requiring a longer timeframe. Future strategies must therefore focus on fostering innovation, developing value-added services, and enhancing the visitor experience to fully capitalize on the participation base and ensure economic sustainability in the post-Olympic era.

This study is not without limitations. The concentration on the 2015–2019 pre-Olympic period captures the build-up phase but necessitates follow-up research to assess the sustainability of these development trends beyond the immediate Olympic horizon. Furthermore, while the national-level analysis reveals macro-trends, future research incorporating more granular regional data could provide deeper insights into the varied local impacts and the role of climatic and geographic factors (50). Finally, enhancing the international comparability of the index by benchmarking against FIS standards and best practices from established winter sports nations would strengthen its global relevance.

5 Conclusion

This study set out to construct a scientifically-grounded evaluation system to measure the holistic development of snow sports in China, a national priority following the successful bid for the 2022 Beijing Winter Olympics. To achieve this, a mixed-methods approach was employed, whereby insights from expert interviews were analyzed via grounded theory to develop a comprehensive three-level indicator system. The application of a combined weighting method to panel data from 2015 to 2019 then enabled the computation of a unified development index, with panel regression models subsequently validating the system's utility and probing key impact mechanisms. The empirical findings reveal a period of remarkable transformation, with the overall development index surging by a factor of 10.92, catalyzed by strategic Olympic investment. This growth was most pronounced in mass participation, underscoring the success of policy-driven engagement, though significant gains were also observed in competitive and economic dimensions.

The implications of these findings are twofold. Theoretically, this research provides a validated model that contributes to the understanding of sport development pathways, particularly in the context of mega-events. Practically, it offers policymakers and stakeholders an evidence-based framework for strategic planning and resource allocation in the post-Olympic era, highlighting the proven efficacy of targeted investment while also sounding a note of caution regarding the inhibitory effect of high costs on long-term sustainability. Consequently, future strategies must evolve from building infrastructure to optimizing operational efficiency and enhancing the consumer experience to foster a resilient and self-sustaining market.

It is important to acknowledge the limitations of this work, which in turn illuminate pathways for future research. The concentration on the pre-Olympic build-up phase necessitates longitudinal studies to track the sustainability of these trends beyond the immediate event horizon. Furthermore, the national-level scope of the analysis, while revealing macro-trends, invites more granular investigation into the disparate regional impacts shaped by local factors. Extending this line of inquiry through international benchmarking against global standards would further enhance the model's applicability. Thus, future research should prioritize these longitudinal, regional, and comparative dimensions to build upon the foundation established here.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics statement

All experts and scholars participating in the research were fully informed of the purpose, content and methods of the research, and expressed informed consent by signing the informed consent form, clearly indicating that they voluntarily participated in the interviews and agreed to use their data for the purposes of this research.

Author contributions

YN: Software, Formal analysis, Data curation, Writing – review & editing. XH: Writing – original draft, Writing – review & editing, Investigation, Methodology. QS: Supervision, Investigation, Resources, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research is supported by the Key Project of the National Social Science Fund of China: Research on the Impact of the 2022 Winter Olympics on the Development of Snow Sports in China (Grant No. 16ATY004).

Acknowledgments

Thanks to all authors for their contributions.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspor.2025.1619136/full#supplementary-material

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