Efficiency is most commonly measured using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). SFA is suited for efficiency calculations involving multiple inputs and a single output, while DEA accommodates cases with multiple inputs and outputs. One limitation of conventional DEA is that it can only determine the number of effective decision-making units (DMUs), but it does not support comparisons between those efficient DMUs. The Slacks-Based Measure (SBM) super-efficiency model, which accounts for slack variables, addresses this limitation by allowing for the comparison of efficient DMUs. The SBM model was employed in this study to calculate and compare tourism efficiency across the three provinces in Northeast China and their cities. Since the super-efficiency model produces only non-negative results, the Tobit model is appropriate for regression analysis. This study therefore used a Tobit model to investigate the factors influencing tourism efficiency across the three provinces in Northeast China.

The accuracy of tourism efficiency calculations depends largely on the scientific rigor of the indicators and data selected. In developing the measurement indicator system for tourism efficiency across the three provinces in Northeast China, this study grounded its approach in the fundamental economic theory, aligned with both national and regional policies for promoting high-quality tourism development in Northeast China, and gave full weight to data reliability through a multifaceted indicator selection process. Considering data completeness and availability, this study finally selected 34 prefecture-level cities in Liaoning, Jilin, and Heilongjiang provinces in Northeast China as the case study regions.

In terms of input indicators, capital, labor, and land are the most fundamental factors of production in economics (Ji et al., 2021). Given the service-oriented nature of the tourism industry, which prioritizes service and tourist experience and relies far less on land resources (Zhao, 2024), land was not considered in this study. For labor input, due to the unavailability of actual employment data specific to the tourism industry, this study approximated the tourism-related workforce by aggregating the workforce employed in its pillar sectors, namely accommodation and transportation, as well as the culture, sports, and entertainment sectors. Regarding capital input, the tourism industry is characterized by a high degree of integration and a broad range of contributing factors (Choi et al., 2021). Based on the six fundamental factors of the tourism industry—food, accommodation, transportation, sightseeing, shopping, and entertainment—and to align with policies promoting cross-sectoral integration, this paper incorporated food and accommodation facilities, sightseeing facilities, and transportation facilities as the capital input indicators. These were respectively measured by the number of star-rated hotels, the number of 4A- and 5A-rated tourist attractions, and the total length of regional highways. In addition, the Domestic Tourism Enhancement Plan (2023-2025), which was issued by the Ministry of Culture and Tourism in 2023, identified improving tourism quality and elevating tourists’ consumption experience and satisfaction as key objectives, while also emphasizing the development of cultural infrastructure to support tourism. Accordingly, this study incorporated comprehensive reception and cultural capital into the tourism input indicator system, which were measured respectively by the number of travel agencies and the combined total of libraries, museums, and gymnasiums.

With respect to output indicators for the tourism industry, tourism output should essentially encompass all the needs and service benefits experienced by tourists throughout their journey. However, considering indicator measurability and data availability, most existing studies adopt the number of tourist visits and total tourism revenue as indicators of tourism service output. On this basis, this study categorized tourism output indicators into scale output and revenue output, which were measured respectively by total tourist visits and total tourism revenue.

All data used in this study were sourced from the China Statistical Yearbook, the China Cultural and Tourism Statistical Yearbook (formerly the China Tourism Statistical Yearbook), and the China City Statistical Yearbook, as well as the statistical yearbooks and national economic and social development bulletins of the relevant provinces and municipalities. Missing values were filled in according to the nature of each dataset, using approaches such as mean substitution, Last Observation Carried Forward (LOCF), Baseline Observation Carried Forward (BOCF), and linear interpolation.

This study employed MATLAB and the SBM model to calculate tourism efficiency in the three provinces in Northeast China from 2003 to 2022. The results revealed that while tourism efficiency exhibited a generally spiralling upward trend over time, it remained in an inefficient state overall during the study period (Figure 1). Specifically, before 2015, the average tourism efficiency across the three provinces in Northeast China exhibited an initial increase followed by a decline, and it remained generally low with minor fluctuations, consistently hovering between 0.2 and 0.4. From 2015 to 2018, tourism efficiency increased markedly, with the average holding steady at 0.5. From 2019 onward, tourism efficiency experienced greater fluctuations and reached its peak in 2021.

At the provincial level, the three provinces demonstrated significant disparities in tourism efficiency. However, these disparities steadily narrowed from 2015 to 2022, indicating a trend toward greater uniformity. Liaoning Province consistently outperformed the regional average in tourism efficiency and followed a temporal pattern of initial growth followed by a decline. The development of tourism efficiency in Jilin Province can be divided into two distinct stages: the stable stage before 2015 and the optimization and improvement stage from 2015 onward. The evolution of tourism efficiency in Heilongjiang Province followed a pattern similar to that of Jilin Province, but with smaller fluctuations. Before 2008, tourism efficiency in Heilongjiang Province showed modest growth, followed by a period of slow but continuing increase. A more pronounced and sustained improvement began to emerge after 2014. Over the past two decades, Liaoning Province consistently maintained a comparatively high level of tourism efficiency in Northeast China; Jilin Province experienced the greatest variability, starting at a lower level but growing quickly in the past two decades; and Heilongjiang Province exhibited a comparatively steady upward trend in tourism efficiency. According to the lifecycle theory for tourism destinations and the observed development patterns of each province, Northeast China as a whole is currently in the tourism development stage, while Liaoning Province is in the maturity stage, and Jilin and Heilongjiang provinces are in the stage of rapid growth.

At the city level (Figure 2), all cities showed some level of improvement in tourism efficiency in 2022 relative to 2003. Throughout the 20-year span, the number of cities classified as efficient in each year consistently exceeded 10, with an annual average of 12 cities, accounting for about one-third of the total. While the number of efficient cities varied from year to year, the overall trend was upward, punctuated by a sharp increase occurring in 2015. Among the cities, Changchun stands out with both overall high tourism efficiency and rapid growth. It is followed by some cities in Heilongjiang Province, including Harbin, Jixi, and Suihua, which have shown steady improvements. In contrast, high-efficiency tourism cities in Liaoning Province such as Shenyang, Anshan, Liaoyang, Benxi, and Dandong have shown a noticeable slowdown in the pace of tourism efficiency growth.

In 2015, the former “4+1” urban tourism consortium in Northeast China (Changchun, Harbin, Shenyang, Dalian, and Anshan) jointly launched the Northeast China Tourism Promotion Alliance. This initiative advanced the overall tourism branding of Northeast China, expanded both domestic and international market development, and facilitated resource sharing and mutual benefit among the cities in Northeast China. As a result, it contributed to a substantial improvement in tourism efficiency and shifted Northeast China away from its long-standing pattern of a low-efficiency tourism economy. At the provincial level, Liaoning Province experienced a steady increase in tourism efficiency before 2015, remaining above 0.5 and gradually approaching 1. However, from 2015 onward, it entered a downward trajectory, with the rate of decline significantly outpacing previous growth and marked by three pronounced drops. As reported in the 2016 document entitled The Cultural and Tourism Industry in Liaoning Province: Development Status, Challenges, and Solutions by the People's Government of Liaoning Province, a total of 242.60 million yuan was invested between 2013 and 2015 by the provincial treasury. Although fiscal support for culture and tourism increased during this period, the short-term investment failed to yield immediate results, which may have contributed to the noticeable decline in tourism efficiency. The global health crisis that emerged at the end of 2019 dealt a severe blow to the tourism industry in Liaoning Province, leading to a pronounced downturn. The resulting market instability caused tourism efficiency to exhibit an erratic pattern overall. In July 2014, Jilin Province formally articulated its overall objective and key tasks for elevating tourism to a pillar industry. This development objective provided clear direction and a rallying call for a new wave of tourism development in the province and powerfully advanced the development of its tourism industry. After this, the “Service Sector Campaign” further propelled Jilin's evolution from a tourism resource-rich province into one characterized by a robust tourism economy, leading to continuous improvements in the efficiency of its tourism industry. Heilongjiang Province and Jilin Province are geographically adjacent and share similar patterns of tourism development, but they remain in an intense rivalry. Initially, Heilongjiang Province capitalized on its solid industrial foundation while overlooking the development of its tertiary industry. However, with the advent of the information age and increasingly market-driven dynamics, its resource advantages came to the forefront and attracted growing tourist interest. Meanwhile, local governments seized this opportunity by promoting the ice-and- snow economy and launching cross-border tourism services. They implemented a series of policies to gradually enhance infrastructure, improve tourism services, and intensify promotional efforts, resulting in a steady increase in tourism efficiency.

To visually present the distribution of tourism efficiency across the three provinces in Northeast China and investigate its spatial evolutionary characteristics, distribution maps for the period from 2003 to 2022 were generated using ArcGIS software (Figure 3). Previous scholars typically used threshold values of 0.25, 0.5, 0.75, and 1 to classify regional efficiency levels (Sun et al., 2024). Considering the generally low tourism efficiency in Northeast China and the pronounced polarization within the region, and in order to both highlight intra-regional differences and keep the number of categories manageable, this study divided tourism efficiency across the three provinces in Northeast China into five levels based on efficiency scores: low efficiency (0-0.3), relatively low efficiency (0.3-0.6), moderate efficiency (0.6-0.9), relatively high efficiency (0.9-1.2), and high efficiency (≥1.2).

Before 2019, tourism efficiency in Northeast China was highly polarized, with cities like Changchun, Harbin, Dalian, and Liaoyang consistently at the forefront. High-efficiency tourism zones showed a northward tendency. Although these zones remained largely in the south, industrial development gradually narrowed the regional disparities in tourism efficiency. This indicates that the overall allocation of tourism resources has improved across the three provinces in Northeast China, leading to a more balanced industry development and enhanced integration of tourism with related regional industries. Since the founding of the People's Republic of China, regional integration has been consistently advanced to foster inter-regional coordinated development. This coordinated regional economic growth has, in turn, propelled the development of all-region tourism, contributing to a more balanced distribution of tourism efficiency. Specifically, the high-efficiency zones are mainly clustered in the southeastern and central parts of Northeast China, likely due to urban geographic characteristics. The southeastern area is dominated by port and coastal cities with convenient international exchange, developed economies, high accessibility, and advantageous spatial proximity to both domestic and overseas source markets. The central area, anchored by the provincial capitals Changchun and Harbin, serves as the economic, cultural, political, and transportation hub of the region. It commands premium resources, possesses core appeal unmatched by other cities, and enjoys substantial advantages in both tourism resource appeal and tourism industry management. After 2019, the spatial distribution of tourism efficiency underwent a significant change in which cities with higher levels of economic development were no longer those with the top tourism efficiency, while high-efficiency cities became more concentrated around provincial capitals. This shift suggests that the COVID-19 pandemic had a more pronounced impact on destinations with larger-scale tourism development.

To further investigate whether tourism efficiency in Northeast China exhibits spatial correlation, ArcGIS software was used to perform a global spatial autocorrelation analysis of the tourism efficiency in Northeast China. The results are presented in Tables 2 and 3. From 2007 to 2019, Moran's I values were consistently positive, and their P-values were all below 0.05, indicating a significant spatial agglomeration effect of tourism efficiency within the region. After 2019, this spatial agglomeration effect was no longer significant, indicating that the COVID-19 pandemic also impacted the spatial agglomeration effect of tourism efficiency. A subsequent high/low clustering analysis of tourism efficiency in Northeast China yielded positive General G statistics with P-values below 0.05 during the periods of significant spatial clustering. This indicates a clear high-value clustering pattern of tourism efficiency in the region, which is consistent with the earlier results regarding tourism efficiency distribution.

Based on spatial autocorrelation and the geographic distribution of tourism efficiency, two spatially high-efficiency clusters can be identified in Northeast China: the Changchun-Harbin urban agglomeration centered on Changchun and Harbin, and the Southeast Liaoning urban agglomeration dominated by coastal and port cities. The agglomeration effect of these two high-efficiency clusters followed a rise- then-fall trajectory and exhibited an overall inverted-V-shaped development pattern over time. The year 2012 was the turning point. After 2012, the high-efficiency agglomeration effect gradually waned, inter-city disparities in tourism efficiency within each cluster narrowed, and the efficiency of the regional tourism industry steadily approached a balanced state. A comprehensive comparative analysis of tourism efficiency trends shows that, as the high-efficiency agglomeration effect waned, tourism efficiency in Northeast China has been progressively becoming more coordinated.

The tourism industry is comprehensive, and its efficiency is shaped by multiple factors. Drawing on prior studies and taking into account the specific characteristics of Northeast China, such as the prevalence of resource-depleted cities, a robust industrial and agricultural foundation and its status as a border region, this study investigated the key factors influencing tourism efficiency across the three provinces in Northeast China from nine perspectives: endowment of tourism resources (res), level of economic development (eco), opening-up level (ope), urban industrial structure (ind), informatization level (inf), marketization level (mar), level of educational development (edu), infrastructure development (infr), and geographical location (geo) (Sheng and Liu, 2020; Guo et al., 2021; Yu and Zuo, 2022; Sheng et al., 2023). To provide a systematic and comprehensive assessment of the endowment of tourism resources in Northeast China, this study selected certain indicators including the numbers of national nature reserves, national scenic areas, parks, key rural tourism villages (towns/townships), national historical and cultural towns, national historical and cultural blocks, and 4A- and 5A-rated tourist attractions. The relevant data and their descriptive statistical analysis results are presented in Table 4.

Stata was employed to run random-effects panel Tobit models on the factors influencing tourism efficiency for Northeast China as a whole and for each of its provinces. The results (Table 5) reveal that urban industrial structure, opening-up level, informatization level, marketization level, endowment of tourism resources, level of educational development, infrastructure development, and geographical location all exert significant influences on tourism efficiency in Northeast China, whereas the level of economic development does not exhibit a significant impact.

The results indicate that the level of openness, degree of marketization, optimization of urban industrial structure, and infrastructure development have significant positive effects on the tourism economic efficiency in Northeast China. Among these, openness and marketization have been widely recognized in academic circles as the core driving forces behind tourism industry efficiency. Specifically, openness contributes to a favorable environment for the development of regional tourism by facilitating capital agglomeration, talent mobility, managerial knowledge spillovers, and the introduction of international capital. Meanwhile, improvements in marketization significantly enhance overall tourism industry efficiency through more effective resource allocation, the promotion of labor specialization, and the stimulation of vitality among market entities. Leveraging its geographic advantages—bordering Russia, North Korea, South Korea, and Japan—as well as its role as a central hub in the Northeast Asia region, Northeast China enjoys inherent locational benefits for cross-border tourism cooperation and international exchanges. At the same time, industrial upgrading, marked by a rising share of the tertiary sector, has driven the continuous improvement of tourism infrastructure and supporting service systems. The resulting enhancement in service quality strengthens the region's tourism appeal, further stimulating tourism demand and output growth, and fostering a virtuous cycle of “industrial upgrading-service optimization-efficiency improvement”.

However, the endowment of tourism resources, geographical distribution, level of informatization, and educational development exert significant restraining effects on the tourism economic efficiency in Northeast China. From the perspective of resource economics, although high-grade tourism resources possess strong attractiveness, their spatial immobility and the high costs required for development and management often lead to a “resource curse” phenomenon. In the Northeast, while winter ice-and-snow tourism resources have been relatively well-developed, summer resort and border tourism resources still face issues such as product homogenization, low-end service offerings, and imbalanced spatial allocation. These challenges have hindered the full realization of the economic value of high-quality resources. In terms of geographical location, tourism efficiency demonstrates a clear spatial clustering effect. Areas within a half-hour economic radius of central cities benefit from proximity to source markets and achieve efficiency premiums by developing cost-effective, high-efficiency models such as urban micro-vacations and suburban rural tourism. In contrast, due to poor accessibility, the remote areas often fall into efficiency troughs. The negative impacts of informatization and education manifest as dual constraints. On one hand, the outflow of talent results in an insufficient supply of professional tourism service personnel, thereby limiting the standardization of services and the modernization of management. On the other hand, the mismatch between investment in digital infrastructure and its actual application effectiveness fails to meet the tourists’ demand for intelligent experiences and cannot support refined industry management. Instead, it exacerbates competition in the regional tourism markets.

It is noteworthy that the level of economic development does not exert a significant effect on tourism efficiency in Northeast China, distinguishing it from other regions in the country. From the perspective of industrial economics, this region's tourism sector remains in a resource-driven development phase. Its characteristic resources centered on ice-and-snow and summer resort tourism have not been fully exploited, and weak infrastructure combined with insufficient factor inputs has prevented tourism from becoming deeply embedded in the regional economic system. According to Rostow's stages-of-growth theory, a nascent industry depends little on the broader economic base; and only upon reaching maturity does economic development emerge as a key driver of efficiency gains. As a traditional industrial heartland, Northeast China boasts a strong agricultural and manufacturing foundation but lags in service-sector development. The tourism industry has yet to form a complete value chain or achieve economies of scale, and these industrial-stage characteristics attenuate any direct impact of overall economic development on tourism efficiency.

Further regional heterogeneity analysis indicates that the mechanisms influencing tourism efficiency vary significantly among the three northeastern provinces. While the advantage of border location consistently promotes tourism efficiency throughout the region, the dominant influencing factors differ among the provinces. In Liaoning Province, tourism efficiency is primarily influenced by the level of openness, urban industrial structure, economic development, and informatization. The dual-core urban cluster centered around Shenyang and Dalian generates strong economic spillover effects, and the advanced industrial structure supports tourism efficiency. However, the diminishing marginal returns on informatization investment warrant more attention. The application of digital technologies has led to rising marketing costs and the dispersion of tourist flows, thereby weakening the competitive advantage of traditional tourist destinations. In Jilin Province, tourism efficiency is more sensitive to marketization, economic development, openness, and educational development. Under its dual-engine model driven by both market forces and resource endowment, this province faces pronounced structural contradictions between the monopolistic nature of top-tier resources—such as Changbai Mountain—and the upgrading demands of service consumption. Moreover, rising factor prices resulting from rapid economic growth have created additional constraints on efficiency improvement. In Heilongjiang Province, the key influencing factors include openness, industrial structure, informatization, marketization, and education level. The transformation of its industrial heritage and the exploitation of border tourism potential have been facilitated by adjustments to this province's industrial structure. Nonetheless, challenges remain in fully harnessing the potential of the digital economy, and the persistent developmental imbalance between the northern and southern regions still requires urgent resolution.

In summary, the differences among the three provinces are primarily reflected in their respective modes of factor combinations: Liaoning follows an “economy + structure” driven model, Jilin relies on a “market + resource” approach, and Heilongjiang adopts an “industry + education” synergy. These patterns vividly reflect the unique resource endowments and developmental stages of each region, which provides a scientific basis for formulating differentiated tourism development strategies. Liaoning should focus on strengthening the integration of digital technology with culture and tourism through innovative models, while Jilin needs to address the structural contradiction between resource dependence and consumption upgrading, and Heilongjiang should accelerate the development of industrial heritage tourism and promote service sector upgrading through educational empowerment.

Along the Chinese path to modernization, even as the country encounters new development stages and opportunities, uneven and insufficient regional economic development remains an issue that cannot be ignored. In regions endowed with rich cultural capital but burdened by a high proportion of resource-depleted cities, industrial transformation and development are imperative. These regions typically possess substantial cultural capital and premium natural assets and exhibit distinctive local characteristics, so they offer an excellent resource foundation for the development of the tourism industry. Therefore, such regions should capitalize on their distinctive regional characteristics, align with the “Grand Tourism” development framework and the requirements for high-quality regional development, profoundly explore the uniqueness of their cultural heritage, integrate cultural and tourism resources, and promote local economic prosperity through the development of the tourism industry. In developing the tourism industry, priority must first be given to attracting and nurturing talent to shore up regional shortcomings. Second, the driving potential of advantaged areas should be fully leveraged, with economic core zones stimulating growth in peripheral zones to achieve balanced regional economic development. Lastly, policies and regulations should be further refined. Policy guidance and support must be strengthened at the macro level to provide robust safeguards for the healthy development of the tourism industry.

This study addresses the major limitation of existing tourism efficiency research, which has largely focused on regions with well-developed tourism economies and consequently offers limited generalizability. Moreover, this study examines strategies for achieving high-quality development in resource-depleted regions from the perspective of tourism efficiency, thereby offering valuable practical reference for their economic transformation. Despite these contributions, this study still includes several potential research areas that warrant further investigation. First, this study did not account for undesirable outputs such as carbon emissions and other environmental issues that may emerge during tourism industry development. Given the growing attention to these issues within the tourism industry, now hailed as a “green industry”, future research should incorporate these environmental indicators into the evaluation system for tourism efficiency. Second, due to data availability constraints, this study covered only up to 2022 and thus could not fully dissect the marked changes in tourism efficiency that occurred during the post-pandemic period. The COVID-19 pandemic not only altered consumption patterns and lifestyles but also profoundly reshaped the operating model of the tourism industry. Therefore, there is a pressing need for more in-depth analysis and research into the underlying mechanisms driving the evolution of tourism efficiency in Northeast China during this key period.

(1) Temporally, tourism efficiency in Northeast China followed a spiralling upward trajectory during the study period. However, overall efficiency remained low and far below effective levels, indicating that this region is currently in the growth stage. The tourism industry in Northeast China overall exhibited three stages of evolution: a stable low-efficiency stage before 2015; a swift efficiency enhancement stage between 2015 and 2018; and a growth stage with significant fluctuations from 2019 to 2022. During the study period, Liaoning Province recorded the highest tourism efficiency, albeit with a relatively modest growth rate, indicating that it has entered a mature stage of tourism development. In contrast, Jilin and Heilongjiang Provinces demonstrated lower tourism efficiency but considerable development potential, suggesting that they remain in the tourism development stage.

(2) Spatially, tourism efficiency in Northeast China shows two high-efficiency clusters—the Changchun-Harbin urban agglomeration and the Southeast Liaoning urban agglomeration. Moreover, tourism efficiency within the region has gradually shifted from a polarized pattern, which was extremely high at cluster cores and extremely low at the margins, toward a more balanced distribution. Tourism efficiency across different regions exhibits a pronounced convergence as the agglomeration effect of high-efficiency zones wanes.

(3) The evolution of tourism efficiency has exhibited a strong temporal alignment with the implementation of policies and measures such as the Northeast China Revitalization and regional cooperation. It has been shaped by factors such as resources, location, industry, science and technology, and education, while it exhibits only a weak correlation with the level of economic development. While tourism efficiency and its influencing factors vary significantly across provinces, they are all closely tied to local policies, hygienic conditions, and the opening-up level.

In summary, to achieve high-quality economic development in the Northeast region, a regional tourism economic joint development system should be established at the macro level, while the “push-pull” strategy can be employed to enhance market competitiveness at the micro level. Meanwhile, each province should explore differentiated development models based on its own characteristics.

In the era of high-quality development, enhancing both the efficiency and quality of tourism industry development, while shifting away from the traditional extensive development model, represents a key pathway toward sustainable tourism development. At present, the regions represented by Northeast China that have long been dominated by industry and agriculture have built up considerable tourism potential and are now poised for take-off, so they stand at a critical juncture in their development. These regions should adhere to the concept of “culture as the cornerstone, green development as the guide”, continually tap into their local cultural heritage, showcase their distinctive humanistic features, implement the “Two Mountains” theory, pursue market-oriented approaches, and enhance policy support to elevate their regional tourism efficiency and achieve high-quality tourism development.

From a macro perspective, the developmental trajectory of the tourism industry in Northeast China is heavily shaped by policies. Within the framework of China's current strategic planning for the tourism industry, the relevant regions should undertake reasonable planning to define their development direction and strategies. These efforts should specifically include the following considerations. First, it is essential to strengthen regional cooperation systems, optimize the allocation of resources, and harness the advantages of regional synergy mechanisms. During the study period, regional joint development demonstrably enhanced tourism efficiency in Northeast China. Therefore, going forward, the region should further strengthen inter-city tourism cooperation, create distinctive regional tourism brands, and construct a joint system for regional tourism development. Through multi-dimensional, multi-faceted cooperation in brand building, resource planning, service management, and image promotion, this region can counteract the decline in tourism appeal caused by rising costs. Meanwhile, resource allocation should be optimized to prevent the malicious intra-regional competition and resource waste resulting from product homogenization. Second, it is essential to continuously advance the deep integration of culture and tourism, while simultaneously expanding opening-up. High-quality tourism development requires a strong emphasis on the local cultural core and highlighting local cultural features. Northeast China's unique humanistic features and profound industrial heritage confer upon it an irreplaceable identity. By embracing the principle of “advancing tourism through culture and enriching culture through tourism”, this region can shape its distinctive cultural tourism identity and launch both cultural and industrial tourism initiatives to enhance tourism efficiency. Meanwhile, this region should expand its opening-up, promote its local cultural spirit, and enhance tourism efficiency through cultural exchanges. Third, it is essential to strongly emphasize the construction of ecological civilization and capitalize on the region's advantages in ecological resources. Building on its natural resource advantages, Northeast China should reinforce the “winter snowplay, summer retreat” tourism brand and develop green, eco-leisure tourism region wide. By advancing initiatives in regional ecological civilization construction, it can create a favorable tourism environment, enhance the residents’ well-being, and ensure the sustainable development of the tourism economy. Besides, as one of China's key grain-producing regions, regional efforts to develop its tourism economy should concentrate on rural areas where the opportunities lie. Since tourism efficiency in Northeast China is only marginally influenced by economic development, this region should capitalize on its indigenous qualities and continually promote the high-quality development of rural tourism.

From a micro perspective, the industry's overall layout should address both supply and demand. On the one hand, it must meet market needs, prioritize the tourist experience, and offer demand-driven, high-demand tourism products; on the other hand, it should capitalize on the domestic tourism boom by introducing innovative travel offerings that invigorate the market and elevate regional competitiveness. In addition, greater emphasis should be placed on talent management and the adoption of information technologies to improve service quality in the tourism industry. Local advantages in educational resources should be leveraged to enhance the talent retention mechanism and prevent excessive brain drain. Simultaneously, efforts should be made to strengthen the application of information technology by talent, and its negative impact on regional tourism efficiency should be reversed through the high-efficiency utilization of informationalized services. Lastly, tourism infrastructure, supporting facilities, and policy support should be improved. Under policy guidance, efforts should focus on improving service quality, gradually advancing the digitalization and intelligentization of tourism services, and offering more convenient and comfortable travel experiences. Since it is now in the mature stage of tourism development, Liaoning Province should foster new economic growth drivers in its tourism economy to stay competitive amid the informatization wave of the digital age. Jilin Province, which remains in the development stage of its tourism economy, should prioritize building a strong tourism brand and boosting market competitiveness with a premium brand image, while also taking care to prevent cost increases resulting from economic development. Similarly positioned in the development stage of its tourism economy, Heilongjiang Province should further enhance its infrastructure, capitalize on its resource endowments and geographic advantages, and develop ice-and-snow tourism along with border tourism.