Measurement of Tourism Ecological Efficiency and Analysis of Influencing Factors under the Background of Climate Change: A Case Study of Three Provinces in China’s Cryosphere

Abstract

Against the backdrop of climate change and the “dual carbon” goals, enhancing the ecological efficiency of cryospheric tourism is crucial not only for the high-quality development of the tourism industry itself but also for the protection of the ecological environment and the promotion of green sustainable development in the cryospheric region. In light of this, this study, taking climate change as its background and based on the perspective of carbon emission constraints, integrates multidimensional factors such as “climate change, carbon emission constraints, and cryospheric resources” into a unified measurement framework to construct a model for evaluating the ecological efficiency of tourism in the cryosphere. Specifically, the model considers inputs, expected outputs, and unexpected outputs. Subsequently, employing the super-efficiency slack-based measure (SBM) model, this study measures the tourism ecological efficiency (TEE) of three provinces (Xinjiang, Qinghai, Tibet) in the cryosphere from 2013 to 2021 and utilizes the Malmquist–Luenberger index and gray correlation model to reveal their dynamic changes, efficiency decomposition, and influencing factors. The results indicate that: (1) The overall mean of TEE in the cryosphere is between 0.2428 and 1.2142, Over the study period, the average annual growth rate and corresponding confidence interval were 14.74%, (−8.61%, 64.23%), showing a significant fluctuating growth trend. Among them, Xinjiang stands out, with its mean scores ranging from 0.2418 to 1.6229, surpassing the overall average level of the cryosphere. (2) During the study period, the overall dynamic efficiency of tourism ecology in the cryosphere increased by 21.54%, driven by the synergy of technological progress (TC), pure technical efficiency (PET), and scale efficiency (SE). For each province, the dynamic efficiency of tourism ecology has improved, but to varying degrees. (3) Regarding the driving factors of TEE in the cryosphere, each driving factor is closely related to TEE, ranked from large to small as follows: carbon emission structure, level of economic development, infrastructure, intensity of technological input, industrial structure, resource endowment, and environmental regulation. This article holds theoretical and practical significance for promoting the high-quality development of polar tourism and achieving synergistic progress between the economy and environment.

1. Introduction

As of the end of 2023, Xinjiang had a total of 17 national 5A-level tourist attractions, 1222 travel agencies, and 331 star-rated hotels. Throughout the year, Xinjiang received 265.4403 million visitors, generating a total tourism revenue of RMB 296.715 billion. The region’s topography can be described as “three mountains flanked by two basins”, with the Tianshan and Altai mountain ranges hosting extensive glacial and permafrost areas. Qinghai, by contrast, had 4 national 5A-level tourist attractions, 714 travel agencies, and 264 star-rated hotels. The province welcomed 44.7635 million visitors annually, with a total tourism revenue of RMB 43.064 billion. Qinghai’s landscape encompasses the Qinghai–Tibet Plateau, inland arid basins, and the Loess Plateau, with the Qilian Mountains being a significant area for glacial and permafrost distribution. Tibet had 5 national 5A-level tourist attractions, 390 travel agencies, and 131 star-rated hotels, receiving 55.1697 million visitors annually and generating a total tourism revenue of RMB 65.146 billion. Tibet’s terrain includes the Northern Tibetan Plateau, the Southern Tibetan Valleys, the Eastern Tibetan Highlands, and the Himalayas. The area between the Kunlun Mountains and the southern section of the Tanggula Mountains contains extensive perennial glacial and permafrost regions. In recent years, the tourism industry in the cryosphere has been thriving, serving as a crucial means to fully utilize the advantages of cryospheric resources and fill the gap in regional winter tourism [1]. Cryospheric tourism, based on elements such as glaciers, permafrost, and snow, encompasses glacier tourism, snowscape tourism, permafrost tourism, as well as sightseeing, exploration, scientific research, cultural activities, and sports such as skiing [2]. Cryospheric tourism is mostly situated in remote and economically underdeveloped areas, serving as an important industry for employment and economic growth in these regions [1] With its significant potential in employment generation and promoting regional economic balance, Cryospheric tourism has gradually emerged as a new engine for socio-economic growth in the cryospheric regions under the new normal [3]. In March 2016, China’s General Secretary Xi Jinping emphasized that “green mountains and clear waters are as good as mountains of gold and silver, and ice and snow tourism also holds the same value.” In China, the “Ice and Snow Sports Development Plan (2016–2025)” issued the same year set the fundamental goal of developing ice and snow sports and improving people’s health, emphasizing the need to accelerate the formation of a relatively complete industrial system for ice and snow sports to drive the vigorous development of cryospheric tourism [4]. China’s “Ice and Snow Tourism Development Action Plan (2021–2023)”, issued in 2021, pointed out that ice and snow tourism should establish a more rational spatial layout and a more balanced industrial structure to better meet people’s needs for a better life and further promote the healthy development of the ice and snow tourism industry [5].

However, climate change characterized by global warming and increasing carbon dioxide concentrations has led to a continuous rise in surface temperatures [6,7], gradually emerging as one of the key factors constraining the sustainable development of the cryospheric tourism industry [8]. This phenomenon has resulted in increasingly severe issues such as the sharp reduction of snow and ice resources, as well as the melting of glaciers and permafrost [9,10,11], which not only damages the natural landscape of cryosphere but also diminishes its aesthetic and scenic values, thereby reducing its attractiveness to tourists and causing certain economic losses [12]. At the same time, measures taken to address these problems, such as artificial snow-making and ski resort infrastructure construction, continuously release greenhouse gases, exacerbating the problem of carbon emissions exceeding standards and contradicting China’s “dual carbon” goals [13]. This failure to effectively implement low-carbon environmental protection concepts significantly hinders the sustainable development of cryospheric tourism industry, as it is unable to effectively meet the growing demand for ice and snow tourism while also obstructing the achievement of the “dual carbon” goals. The “dual carbon” goals emphasize China’s commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, embodying the implementation of green development principles and the concept of a community of shared future for mankind, and emphasizing the value orientation of harmonious coexistence between economy, society, and natural environment. This implies that the sustainable development of cryospheric tourism industry will be endowed with new era propositions. Resolving the deep-seated contradictions between tourism industry development and carbon emissions exceeding standards, fully leveraging the advantages of cryospheric resources, promoting tourism-driven poverty alleviation, and achieving coordinated progress between economy and environment have become urgent needs for promoting high-quality development of cryospheric tourism industry under the new normal. As a core indicator reflecting the coordination between the regional human–land system and the sustainable development of the tourism industry, tourism ecological efficiency (TEE) is essentially aimed at resolving the contradiction between regional economic growth and environmental protection, primarily manifested in the two-dimensional equilibrium relationship between economy and environment [14]. In terms of long-term mechanisms, improving TEE has become a key measure for achieving the sustainable development of the cryospheric tourism industry [15].

Against this backdrop, the statistical measurement of TEE has become a major issue of common concern in both academia and policy-making circles. Existing measurement studies are primarily based on the following three paradigms: (1) Single Ratio Method: Representative studies focus on the definition of ecological efficiency by the World Business Council for Sustainable Development (WBCSD). They measure it using the ratio of tourism industry carbon emissions to tourism industry revenue. This approach aims to characterize the level of TEE based on the proportional relationship between resource consumption, environmental costs, and tourism output [16,17,18,19,20]. (2) Index System Method: Representative studies focus on measuring TEE from economic, environmental, and social perspectives. This method includes indicators such as labor, land, energy consumption, and environmental impacts. Often, specific pollutants such as coal and industrial wastewater are used as indicators of energy consumption and environmental impact [21,22,23]. (3) Model Method: Representative studies employ models such as data envelopment analysis (DEA), super-efficiency slack-based measure (SBM), Life Cycle Assessment (LCA), input–output models, etc., to measure TEE. Commonly China used indicators include tourism employment, tourism land use, and tourism value-added as input and output indicators. Models like DEA or SBM are used to measure the TEE level [24,25,26], the world often uses input–output models to evaluate tourism ecological efficiency [27].

The existing literature has made valuable explorations into the measurement of TEE, providing useful references for this study. However, there are still areas that require further expansion. Firstly, the existing literature tends to focus on using coal consumption and tourism pollutant emissions as environmental pollution variables when constructing TEE evaluation indicators [22,23]. There is limited research that explores TEE from the perspective of carbon emission constraints. Secondly, previous studies have mostly concentrated on measuring TEE in single products, single destinations, regions, provinces, and economic zones [17,18,28]. There is insufficient attention given to the cryosphere, particularly regarding the factors driving ecological tourism efficiency in the cryosphere, and the external driving factors affecting ecological tourism efficiency in the cryosphere have not been clarified. Lastly, since single-index measurement is not the optimal input–output set and does not consider various resource consumption, economic output, and environmental damage [29,30], previous studies tended to use DEA models to measure TEE but overlooked the importance of slack variables, limiting the accuracy of decision unit rankings [20,31,32]. Therefore, examining the dynamic changes, decomposition of factors, and influencing factors of TEE in the context of climate change in the cryosphere is of great theoretical and practical significance for promoting high-quality development of the cryospheric tourism industry and achieving coordinated progress between economy and environment.

In light of this, this paper, taking climate change as its backdrop and based on the perspective of carbon emission constraints, integrates multidimensional factors such as “climate change, carbon emission constraints, and cryospheric resources” into a unified measurement framework to construct a model for estimating the ecological efficiency of tourism in the cryosphere. The model considers inputs, expected outputs, and unexpected outputs, aiming to enrich the existing measurement indicator system. Utilizing the super-efficiency SBM model, this paper measures the ecological tourism efficiency of three provinces in the cryosphere, thereby addressing the deficiency of existing research that predominantly focuses on static rather than dynamic studies of TEE in the cryosphere. By employing the Malmquist–Luenberger index method, this paper explores the dynamic changes and efficiency decomposition of ecological tourism efficiency in the three provinces, revealing the dynamic efficiency of decision units over the study period and providing empirical evidence for tailored and precise policies in the three provinces of the cryosphere. Furthermore, taking climate change as its backdrop and based on carbon emission constraints, this paper fully considers the resource endowment, policy orientation, and functional zoning of the three provinces in the cryosphere to select influencing factors. Additionally, employing the gray correlation model identifies external driving factors that enhance the ecological efficiency of tourism in the cryosphere. This approach helps to break the path dependence of low ecological tourism efficiency in the three provinces of the cryosphere and promotes a high-level transition of ecological efficiency in cryospheric tourism.

2. Materials and Methods

2.1. Research Area Overview

The cryosphere refers to a layer of subzero temperature zone continuously distributed on the Earth’s surface with a certain thickness [33]. Its components include glaciers (including ice caps), permafrost (including seasonal permafrost and perennial permafrost), snow cover, river ice and lake ice, sea ice, ice shelves, icebergs, and frozen bodies of water in the troposphere and stratosphere, ranking as the second-largest layer in the Earth’s climate system after the hydrosphere [34,35]. Due to its high altitude and low-temperature characteristics, it is an area that is sensitive to climate change [3,36,37,38]. As of the end of 2022, the permanent population in the research area was 35.46 million, accounting for 2.51% of the national population. The per capita GDP of the tertiary industry was RMB 30,324.31, equivalent to 35.39% of the national average. The annual precipitation was 340.50 mm, and the average winter temperature was −10.60 °C. Regarding the snow and ice tourism resources in the three provinces of the cryosphere, Xinjiang has 20,736 glaciers with a total area of 24,177.15 square meters, Qinghai has 4059 glaciers covering 4387.03 square meters, and Tibet has 21,962 glaciers covering 25,374.57 square meters (data sources: the Second Glacier Inventory Dataset in China). It can be seen that the resources of the snow and ice tourism in these three provinces are very rich and suitable for the development of snow and ice tourism. In terms of the level of snow and ice tourism development in the three provinces of the cryosphere, during the Beijing Winter Olympics, Xinjiang held more than 500 small and diverse ice and snow events around the theme of “welcoming the Winter Olympics, loving ice and snow”, with a total of over 580,000 participants. Xinjiang has continuously organized the “Winter in the Tianshan Mountains” activity for millions of young people for several years. In 2022, Xinjiang had 56 ski resorts, ranking second in the country. At the end of 2021, Qinghai received 1.5387 million tourists for winter and spring ice and snow tourism, achieving tourism revenue of RMB 30.2439 million, and driving the employment of nearly a thousand people. By 2022, Qinghai had 10 ski resorts. In 2021, the Nyingchi Sports Training Base in Tibet hosted ice experience activities to welcome the Winter Olympics, including a series of experience projects such as curling and skating, attracting many citizens and students to participate. Lhasa and Nyingchi have been listed as emerging popular destinations for ice and snow tourism. The Luodui Peak Mass Ice and Snow Sports event has become a fixed event in the annual mountaineering schedule. As of now, Tibet’s first ski resort is still under construction, indicating its huge potential in cryospheric resources that urgently need to be converted into economic benefits (The number of ski resorts in Xinjiang and Qinghai comes from the “2022–2023 White Paper on China’s Ski Industry”, the construction of ski resorts in Tibet is available on the website of the Nyingchi Municipal People’s Government, and the ice and snow activities of the three provinces are available via the General Administration of Sports of China, the Qinghai People’s Government and the China Tibetan Netcom).

The unique geographical environment of the cryosphere has fostered a distinctive polar culture (

Table 1. Overview of the unique cryosphere culture.

Province	Key Development Cities/Districts	Primary Ice and Snow Tourism Resources	Representative Scenic Areas/Activities
Xinjiang	Urumqi, Altay (Keketuohai)	Ski resorts, ice and snow landscape resources, ice and snow festivals, skiing culture exhibitions, etc.	Jiangjun Mountain Ski Resort, Keketuohai Scenic Area, Xinjiang Ice and Snow Tourism Festival, Mapibis Snowboard-Making Skills Exhibition, “Winter Splendor”-themed tourism activities, etc.
Qinghai	Haixi Mongolian-Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture	Ski resorts, ice and snow landscape resources, ice and snow tourism activities, winter cultural tourism, etc.	Kangle Mountain Resort, Kekexili, Gangshika Snow Mountain, Hexi Ancient Road Ice and Snow, Ice and Snow Light Show, “Winter Tour of Xining” Ice and Snow Fun Tour, Ice and Snow Fireworks Winter Yak Butter Tea, “Forge ahead on a new journey and make contributions in the new era”, and other winter cultural tourism activities.
Tibet	Lhasa, Nyingchi	Ice and snow landscape resources, tourist resorts, “Winter Tour of Tibet” product exhibitions and tourism activities, etc.	Nyenchen Tanglha Mountains, Yangbajing “Blue Heaven” and Xiangxiong Meiduo Tourist Resort, Midui Glacier Tourist Area, “Winter Tour of Tibet, Sharing the Third Season of the Earth” activity, “Ice and Snow Wonderland, Warm Winter Health” 2021, “Winter Tour of Tibet” product exhibition, etc.

Data Sources: According to the official website of the provincial culture and tourism bureau and industry website data collation (https://wlt.xinjiang.gov.cn/, https://whlyt.qinghai.gov.cn/, https://www.tibet3.com/, all accessed on 3 November 2023).

), providing favorable conditions for the construction of ski resorts and the development of ice and snow tourism industries. Numerous ice and snow tourism festivals or events have been organized around these cultural elements [8,39]. Ski resorts not only drive the development of ice and snow tourism but also highlight the theme of “Silk Road culture + ice and snow tourism + ethnic customs” in the cryosphere, promoting the organic integration of ice and snow sports with polar culture [1]. Regarding specific data on polar tourism, detailed and reliable provincial-level data on ice and snow tourism revenue and tourist numbers in Xinjiang, Qinghai, and Tibet are currently difficult to obtain. For instance, industry reports and research literature often only provide data for certain provinces, with detailed data for regions like Tibet and Qinghai being extremely scarce, necessitating the use of more readily available overall tourism industry data as substitutes. In terms of tourist reception numbers in the three provinces of the cryosphere (Figure 1), Xinjiang’s reception volume far exceeds that of Qinghai and Tibet, indicating a higher popularity of ice and snow tourism in Xinjiang, while the level of ice and snow tourism development in Qinghai and Tibet still needs further improvement. Based on the above analysis, this paper selects Xinjiang, Qinghai, and Tibet as the research areas for the study of TEE in the cryosphere, conducting systematic research on the measurement, dynamic changes, factor decomposition, and influencing factors of TEE in this region, in order to provide scientific evidence for adapting to polar changes, promoting regional scientific development, controlling carbon emissions from the polar tourism industry, and promoting green economic development in local areas. For better facilitation of related research, the glaciers and ski resorts of the three provinces are depicted in Figure 2.

2.2. Evaluation Indicator System

Existing literature has focused on the perspectives of capital and labor, using the hotel industry and the travel agency industry as the two major pillar industries of tourism as input indicators to construct a framework for measuring TEE. The reason for this is that star-rated hotels and travel agencies are important indicators for measuring the development of the tourism industry, and data collection in these areas is robust. However, this indicator system has obvious blind spots: it neglects the potential negative impact of tourism on the environment. Building upon this, some scholars have focused on economic, environmental, and social perspectives, using variables such as coal and industrial wastewater as proxies for energy consumption and environmental impact to measure TEE [22,23,40]. However, pollution-related indicators are relatively narrow and fail to reflect the progress and overall effectiveness of tourism industry green development practices. Measuring from the perspective of carbon emissions constraints can effectively complement the shortcomings of this framework. The resource endowment of the cryosphere is a concentrated reflection of its regional advantages. The existing literature has focused on using glaciers, snow cover, ski resort scales, the number of ice and snow tourist areas, the frequency of holding festivals, and the frequency of winter sports events as input levels for measuring the efficiency of tourism cities in the cryosphere [38,41,42]. However, considering the availability and completeness of data and combining the objective fact that glaciers and snow cover are the core tourism resources in the cryosphere [43], glaciers and snow cover are regarded as tourism resources in the cryosphere and are considered together with the number of star-rated hotels and travel agencies as input variables for tourism resources. Tourism employment serves as the labor input variable in the tourism industry, while the fixed asset investment in the tertiary industry and tourism energy consumption serve as capital input and energy consumption in the tourism industry, respectively. Based on this, this paper draws on the research of Guo Lijia et al. [24], Yang Chunmei et al. [38], and Kang Yunjie et al. [3], taking carbon emissions constraints as the logical starting point and combining the characteristics of cryosphere resources to characterize TEE. In the process of constructing the secondary indicator system, it adheres closely to the essential characteristics of the operation of the polar tourism industry, comprehensively considers negative environmental benefits such as manpower, resources, capital, energy consumption, and carbon emissions, and constructs a comprehensive evaluation indicator system for TEE in the cryosphere based on three major aspects: input, expected output, and unexpected output. See

Table 2. Evaluation indicator system for tourism ecological efficiency.

Category	Indicator	Indicator Representation
Input	Tourism resource input	Number of star-rated hotels
		Number of travel agencies Glacier area (km2) Glacier area coverage rate (%) Snow depth (cm) Maximum snow days (days)
	Tourism labor input	Number of tourism practitioners (10,000 persons)
	Tourism capital input	Fixed asset investment in the tertiary industry (RMB 100 million)
	Tourism energy consumption	Tourism energy consumption (PJ)
Expected output	Tourism Income Total tourist arrivals	Total tourism revenue 1 (RMB 100 million) Total tourist arrivals 2 (people)
Non-expected output	Tourism carbon emissions	Tourism carbon emissions (10,000 tons)

¹ The reasons for using total tourism revenue instead of specifically using revenue from ice and snow tourism are as follows: (1) Currently available data only provide nationwide annual revenue from ice and snow tourism, with missing data for provinces such as Xinjiang, Qinghai, and Tibet. (2) The existing literature [44,45] indicates that tourism resource development in western regions primarily aims to highlight distinctive features. Provinces like Xinjiang, Qinghai, and Tibet integrate ice and snow, folk customs, and culture to create unique ice and snow tourism products, which heavily rely on their glaciers and snow. Ice and snow tourism in these provinces emphasizes the theme of “Silk Road culture + ice and snow tourism + ethnic customs”. (3) The existing literature also points out that revenue from ice and snow tourism accounts for a relatively small portion of total tourism revenue. Therefore, total tourism revenue and total tourist numbers are used as variable substitutes for revenue and the number of tourists specifically engaged in ice and snow tourism. ² The reasons for using total tourist numbers instead of specifically using the number of skiers are as follows: (1) Missing data for provinces. (2) The number of skiers is not available on an annual basis; it spans from November of one year to March of the following year, making it non-annual data.

for details. For more information on their raw data, see

Table A1. Raw data of tourism eco-efficiency.

Time	Province	Star-Rated Hotels	Travel Agencies	Glacier Area	Glacier Area Coverage Rate
2013	Qinghai	125	217	4387.03	0.607369
2013	Xinjiang	385	414	24177.15	1.452168
2013	Xizang	125	102	25374.57	2.109625
2014	Qinghai	141.1755	232.3513	24628.09	3.409676
2014	Xinjiang	358.8211	421.5657	24381.81	1.464461
2014	Xizang	141.1755	99.99931	24137.99	2.006817
2015	Qinghai	152.7608	230.108	23566.45	3.262696
2015	Xinjiang	345.162	428.3102	23330.79	1.401333
2015	Xizang	152.7608	189.5007	23097.48	1.920309
2016	Qinghai	72.03955	218.9623	22418.29	3.103737
2016	Xinjiang	331.7611	393.3739	22194.11	1.333059
2016	Xizang	72.03955	194.3172	21972.17	1.826751
2017	Qinghai	151.1492	264.9775	21411.22	2.964311
2017	Xinjiang	300.4323	309.7625	21197.1	1.273176
2017	Xizang	151.1492	234.1879	20985.13	1.74469
2018	Qinghai	148.0375	448.6817	20347.6	2.817056
2018	Xinjiang	274.1436	524.5281	20144.12	1.20993
2018	Xizang	148.0375	283.2817	19942.68	1.658021
2019	Qinghai	183.8214	457.3335	19186.14	2.656256
2019	Xinjiang	263.7438	479.5341	18994.28	1.140866
2019	Xizang	183.8214	275.2881	18804.33	1.56338
2020	Qinghai	154.2235	456.6056	18163.44	2.514666
2020	Xinjiang	299.7828	569.2407	17981.8	1.080053
2020	Xizang	154.2235	268.5915	17801.98	1.480045
2021	Qinghai	162.2885	322.8596	17466.22	2.418139
2021	Xinjiang	312.5555	562.4282	17291.55	1.038594
2021	Xizang	162.2885	202.6459	17118.64	1.423232
Time	Province	Snow Depth	Maximum Snow Days	Tourism Practitioners	Fixed Asset Investment in the Tertiary Industry
2013	Qinghai	24.51	45.1	28.71043	1131.54
2013	Xinjiang	58.47	52	89.93874	3387.72
2013	Xizang	477.21	62.5	32.34661	583.57
2014	Qinghai	21.63711	40.2	29.83901	1456.549
2014	Xinjiang	73.27401	51.4	73.6268	4498.087
2014	Xizang	954.9542	65.1	36.09089	692.4462
2015	Qinghai	21.05778	31.7	31.72214	1586.846
2015	Xinjiang	96.74204	47.5	114.5824	5011.926
2015	Xizang	930.6418	36.8	52.65123	962.3155
2016	Qinghai	21.17584	5.9	36.04916	1963.04
2016	Xinjiang	46.48447	26.6	161.3634	5314.036
2016	Xizang	899.101	45.4	59.43912	1197.582
2017	Qinghai	21.32882	8.2	40.39584	2286.7
2017	Xinjiang	68.11042	13.1	201.2574	7469.353
2017	Xizang	864.8531	33.4	62.65209	1454.932
2018	Qinghai	20.36887	36.3	47.28004	2335.929
2018	Xinjiang	36.2692	46.1	252.4538	4930.705
2018	Xizang	779.7009	64.6	50.99961	1503.354
2019	Qinghai	19.82962	51.2	52.19034	2226.884
2019	Xinjiang	66.60196	48.4	302.5644	4791.571
2019	Xizang	449.359	71	43.51599	1482.847
2020	Qinghai	21.83389	20.4	24.00039	1925.024
2020	Xinjiang	69.78181	35.8	85.69409	5539.889
2020	Xizang	60.13851	40.3	31.28687	1443.882
2021	Qinghai	19.54331	1.1	26.58403	1924.964
2021	Xinjiang	49.53662	2.6	112.6706	6313.849
2021	Xizang	31.29849	40.5	31.10829	1243.502
Time	Province	Tourism Energy Consumption	Total Tourism Revenue	Total Tourist Arrivals	Tourism Carbon Emissions
2013	Qinghai	6.706347	158.54	1780.43	83.15366064
2013	Xinjiang	42.36993	673.24	5205.73	518.9936032
2013	Xizang	3.98358	165.18	1291.06	50.31251704
2014	Qinghai	7.568912	197.9398	1966.241	96.20705619
2014	Xinjiang	40.20763	637.3192	4855.545	502.2106225
2014	Xizang	4.642199	199.9986	1522.676	61.59597731
2015	Qinghai	8.275388	239.8054	2238.622	103.1481578
2015	Xinjiang	35.86189	988.1108	5895.173	475.6933364
2015	Xizang	5.33258	272.5716	1950.629	82.03659188
2016	Qinghai	8.52991	294.1299	2727	111.0212546
2016	Xinjiang	34.31893	1327.992	7679.322	452.5366618
2016	Xizang	6.836879	313.5142	2195.254	109.6151518
2017	Qinghai	9.863441	355.9749	3250.733	128.8450451
2017	Xinjiang	38.9554	1699.933	10007.4	552.9835999
2017	Xizang	9.226823	353.9596	2389.864	156.1685743
2018	Qinghai	10.95374	426.1105	3842.013	146.9986691
2018	Xinjiang	42.66478	2357.37	13729.92	630.2645063
2018	Xizang	11.36157	447.8958	3078.386	193.974802
2019	Qinghai	11.12974	498.4757	4511.324	157.5508053
2019	Xinjiang	40.14508	3225.844	18941.41	574.1691752
2019	Xizang	11.67454	496.6552	3562.894	204.6514083
2020	Qinghai	8.170427	251.1937	2869.424	121.0257347
2020	Xinjiang	24.95944	859.5968	13699.43	377.5939488
2020	Xizang	7.734991	317.4752	3036.826	136.2035988
2021	Qinghai	8.182404	300.4483	3411.853	122.9194625
2021	Xinjiang	27.95653	1215.609	16363.4	401.9828309
2021	Xizang	8.552501	379.4459	3566.431	151.0152524

.

Research on the estimation of tourism energy consumption and carbon emissions is primarily based on two paradigms. Firstly, employing a “top–down” approach which utilizes tourism satellite accounts and regional input–output tables to estimate the energy consumption of the tourism industry, serving as the basis for estimating carbon emissions [46]. Secondly, utilizing a “bottom–up” approach which divides the tourism industry into several sectors, estimating the cumulative carbon emissions of different tourism sectors, and subsequently calculating the total carbon emissions of the tourism industry [20]. On one hand, due to the lack of a national and regional greenhouse gas emission statistical monitoring system in China, compiling and updating tourism satellite accounts and regional input–output table data is challenging, particularly when there are missing environmental data, thus hindering the quantification of greenhouse gas emissions from the tourism industry [47]. On the other hand, the high aggregation and low precision of underlying data result in the “top–down” approach being less accurate and subject to controversy [16]. Therefore, this study adopts a “bottom–up” approach to estimate tourism energy consumption and carbon emissions, following the methodology of Gössling et al. [16], wherein the estimated energy consumption (carbon emissions) of the three major sectors (tourism transportation, accommodation, and activities) is summed to calculate the energy consumption (carbon emissions) of the tourism industry. The specific calculation formula is as follows:

$${\mathrm{Y}}_{\mathrm{Energy}\mathrm{consumption}\left(\mathrm{carbon}\mathrm{emissions}\right)}={\mathrm{Y}}_{\mathrm{Transportation}}+{\mathrm{Y}}_{\mathrm{Accommodation}}+{\mathrm{Y}}_{\mathrm{Activities}}$$

(1)

$${\mathrm{Y}}_{\mathrm{Transportation}}=\sum _{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{T}}_{\mathrm{i}}\times {\mathsf{\omega }}_{\mathrm{i}}\times {\mathsf{\delta }}_{\mathrm{i}}\right)$$

(2)

$${\mathrm{Y}}_{\mathrm{Accommodation}}=\mathrm{N}\times \mathrm{K}\times \mathrm{D}\times \mathsf{\alpha }$$

(3)

$${\mathrm{Y}}_{\mathrm{Activities}}=\sum _{\mathrm{j}=1}^{\mathrm{n}}\left({\mathrm{P}}_{\mathrm{j}}\times {\mathsf{\theta }}_{\mathrm{j}}\right)$$

(4)

In the formula, ${\mathsf{\delta }}_{\mathrm{i}}$, $\mathsf{\alpha }$, and ${\mathsf{\theta }}_{\mathrm{j}}$ represent the energy consumption coefficients (carbon emission coefficients) of the transportation sector, accommodation sector, and activity sector, respectively. Their values are referenced from the study by Wu et al. [48] as detailed in

Table 3. Energy consumption and carbon emission coefficients.

Category	Mode/Activity	Carbon Emission (Unit)	Energy Consumption (Unit)
Transportation	Highway	132 g/person·km	1.8 MJ/person·km
	Railway	65 g/person·km	1 MJ/person·km
	Civil Aviation	396 g/person·km	2 MJ/person·km
	Other	66 g/person·km	0.9 MJ/person·km
Accommodation	General	2.458 g/person·day	155 MJ/person·day
Activities	Sightseeing	417 g/person	8.5 MJ/person
	Leisure Vacation	1670 g/person	26.5 MJ/person
	Business Trip	786 g/person	16.0 MJ/person
	Visiting Friends/Family	591 g/person	12.0 MJ/person
	Other	172 g/person	3.5 MJ/person

for the passenger turnover of different transportation modes (${\mathrm{T}}_{\mathrm{i}}$). ${\mathsf{\omega }}_{\mathrm{i}}$ represents the proportion of tourists using each mode of transportation, with values referenced from the study by Wei et al. [49]; these values are 31.6% for trains, 64.7% for airplanes, 13.8% for cars, and 10.6% for water transport. $\mathrm{N}$ represents the number of beds in star-rated hotels. $\mathrm{K}$ represents the average room occupancy rate. $\mathrm{D}$ equals 365. ${\mathrm{P}}_{\mathrm{j}}$ represents the proportion of tourists participating in activity type j.

2.3. Measurement Method

2.3.1. Super-Efficiency Slack-Based Measure Model

Traditional DEA models such as the Charnes–Cooper–Rhodes model and Banker-Charnes–Cooper model are primarily limited by their radial measurement approach, assuming proportional changes in all inputs and outputs, thereby neglecting the slack improvements that may persist even after proportional adjustments. This oversight can lead to bias in efficiency assessments and a lack of effective methods for dealing with non-expected outputs, thus constraining their accuracy in multidimensional efficiency evaluations. The super-efficiency SBM model proposed by Tone [50] offers several advantages: (1) It can address multi–input–multi–output problems and consider slackness in input–output. (2) The model can effectively distinguish decision-making units that are considered to be fully efficient in traditional DEA, providing a more refined efficiency ranking to ensure the precision of decision-making unit evaluations. (3) Compared to traditional DEA, the super-efficiency SBM model demonstrates more robust assessment results when dealing with extreme or outlier values, thereby offering more valuable references in efficiency measurement. In light of these advantages, this study draws upon Tone’s research and adopts the super-efficiency SBM model, which includes undesirable outputs, to measure TEE. The specific calculation formula is as follows:

$$\min \mathsf{\tau }={\displaystyle \frac{1+\frac{1}{\mathrm{m}}{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{s}}_{\mathrm{i}}^{-}/{\mathrm{x}}_{\mathrm{ik}}}{1-\frac{1}{{\mathrm{q}}_1+{\mathrm{q}}_2}\left({\sum }_{\mathrm{r}=1}^{{\mathrm{q}}_1}{\mathrm{s}}_{\mathrm{r}}^{+}/{\mathrm{y}}_{\mathrm{rk}}+{\sum }_{\mathrm{i}=1}^{{\mathrm{q}}_2}{\mathrm{s}}_{\mathrm{t}}^{\mathrm{b}-}/{\mathrm{b}}_{\mathrm{tk}}\right)}}$$

(5)

$$\mathrm{s}.\mathrm{t}.\sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{ij}}{\mathsf{\mu }}_{\mathrm{j}}-{\mathrm{s}}_{\mathrm{i}}^{-}\le {\mathrm{x}}_{\mathrm{ik}}$$

$$\sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}{\mathrm{y}}_{\mathrm{rj}}{\mathsf{\mu }}_{\mathrm{j}}+{\mathrm{s}}_{\mathrm{r}}^{+}\ge {\mathrm{y}}_{\mathrm{rk}};$$

$$\sum _{\mathrm{j}=1,\mathrm{j}\ne \mathrm{k}}^{\mathrm{n}}{\mathrm{b}}_{\mathrm{tj}}{\mathsf{\mu }}_{\mathrm{j}}-{\mathrm{s}}_{\mathrm{t}}^{\mathrm{b}-}\le {\mathrm{b}}_{\mathrm{tk}}$$

$$1-{\displaystyle \frac{1}{{\mathrm{q}}_1+{\mathrm{q}}_2}}\left(\sum _{\mathrm{r}=1}^{{\mathrm{q}}_1}{\displaystyle \frac{{\mathrm{s}}_{\mathrm{r}}^{+}}{{\mathrm{y}}_{\mathrm{rk}}}}+\sum _{\mathrm{t}=1}^{{\mathrm{q}}_2}{\displaystyle \frac{{\mathrm{s}}_{\mathrm{t}}^{\mathrm{b}-}}{{\mathrm{b}}_{\mathrm{tk}}}}\right)>0$$

$$\mathsf{\mu },{\mathrm{s}}^{-},{\mathrm{s}}^{+}\ge 0;\mathrm{i}=1,2,\dots ,\mathrm{m};\mathrm{r}=1,2,\dots ,\mathrm{q};\mathrm{j}=1,2,\dots ,\mathrm{n}(\mathrm{j}\ne \mathrm{k})$$

In the formula, $\mathsf{\tau }$ represents the TEE value, $\mathrm{n}$ denotes the number of decision-making units (DMUs), and the slack variables for inputs ${\mathrm{x}}_{\mathrm{ik}}$, expected outputs ${\mathrm{y}}_{\mathrm{rk}}$, and undesired outputs ${\mathrm{b}}_{\mathrm{tk}}$ are denoted as ${\mathrm{s}}_{\mathrm{i}}^{-}$, ${\mathrm{s}}_{\mathrm{r}}^{+}$, and ${\mathrm{s}}_{\mathrm{t}}^{\mathrm{b}-}$, respectively. $\mathsf{\mu }$ is the weight vector. A decision unit is considered effective only when $\mathsf{\tau }\ge 1$, indicating that as the value of $\mathsf{\tau }$ increases, TEE improves.

2.3.2. Malmquist–Luenberger Index

To further reveal the dynamic efficiency changes of TEE, this study employs the Malmquist–Luenberger index under the assumption of variable returns to scale to measure the dynamic efficiency and efficiency decomposition of TEE in the three provinces of the cryosphere from 2013 to 2021. This approach aids in uncovering the dynamic efficiency change patterns and driving efficiency decomposition. The decomposition formula is as follows:

$$\mathrm{ML}=\mathrm{T}{\mathrm{E}}_{\mathrm{ch}}\times {\mathrm{T}}_{\mathrm{ch}}=\mathrm{PT}{\mathrm{E}}_{\mathrm{ch}}\times \mathrm{S}{\mathrm{E}}_{\mathrm{ch}}\times {\mathrm{T}}_{\mathrm{ch}}$$

(6)

In the formula, ML represents the total factor productivity, indicating the overall ecological efficiency change of the tourism industry. $\mathrm{T}{\mathrm{E}}_{\mathrm{ch}}$ represents the technical efficiency change, ${\mathrm{T}}_{\mathrm{ch}}$ represents the degree of technological progress, ${\mathrm{PTE}}_{\mathrm{ch}}$ is the pure technical efficiency change, and $\mathrm{S}{\mathrm{E}}_{\mathrm{ch}}$ is the scale efficiency change, where ${\mathrm{T}}_{\mathrm{ch}}$ = ${\mathrm{PTE}}_{\mathrm{ch}}$ × ${\mathrm{SE}}_{\mathrm{ch}}$. When the above indices exceed 1, it indicates an increase in efficiency.

2.4. Data Sources

The input indicators include tourism resource input, tourism labor input, tourism capital input, and tourism energy consumption. The number of star-rated hotels and travel agencies is sourced from the “Tourism Sampling Survey Data” [51]. The glacier area and glacier area coverage rate are based on data from the Second Chinese Glacier Inventory and the average annual change rate of glacier area, which is derived from a compilation of the existing literature [52,53]. The percentage of glacier area to basin/region area represents the glacier resource quantity of specific areas/basins. Snow depth and snow days are obtained from the GLDAS-2No-ah Model Data dataset, the China Meteorological Science Data Center (https://data.cma.cn/), and the 2000–2020 MODIS China snow phenology dataset. The number of tourism practitioners is calculated by multiplying the proportion of tourism income in the tertiary industry income by the number of employees in the tertiary industry. Other data are sourced from the “China Statistical Yearbook”, the “China Tourism Statistical Yearbook”, provincial statistical yearbooks, and national economic and social development statistical bulletins. All value indicators are based on the year 2013 as the base year and adjusted using the Consumer Price Index (CPI) for each year to eliminate the impact of price fluctuations. Individual missing values are supplemented using linear interpolation.

3. Analysis of the Results

3.1. Estimation Results of Tourism Carbon Emissions and Energy Consumption

As shown in Figure 3, the overall evolution trend of energy consumption and carbon emissions in cryosphere tourism during the study period exhibits a pattern of initial increase followed by a decline. From 2013 to 2016, energy consumption slightly decreased while carbon emissions grew slowly, with average annual growth rates of −0.405% and 2.86%, respectively. From 2017 to 2019, carbon emissions continued to increase, rising from 299.3854 million tons in 2017 to 351.4804 million tons in 2019, with an average annual growth rate of 5.49%. Energy consumption increased from 20.7376 PJ in 2017 to 23.6289 PJ in 2019, with an average annual growth rate of 4.45%. In 2020, there was a significant decrease, with carbon emissions at 244.2311 million tons, a year-on-year decrease of 30.51%; and energy consumption at 15.7217 PJ, a year-on-year decrease of 33.46%. Considering the real-world situation and policy factors, the “13th Five-Year Plan for Tourism Development” issued by the State Council in 2016 emphasized the vigorous development of ice and snow tourism and the increase in the number of tourists and tourism revenue, which may have been important factors contributing to the significant increase in energy consumption and carbon emissions from 2017 to 2019. However, the sudden onset of COVID-19 caused nationwide stagnation in the tourism industry, with a significant decrease in the number of tourist trips, becoming a key factor in the decline in 2020.

3.2. Analysis of Measurement Results

As shown in

Table 4. Comprehensive tourism ecological efficiency in the cryospheric area.

Year	Xinjiang	Qinghai	Tibet	Average
2013	0.2559	0.2578	0.2344	0.2494
2014	0.2418	0.2389	0.2477	0.2428
2015	0.2831	0.2714	0.2484	0.2676
2016	0.4291	0.3907	0.2556	0.3585
2017	1.0848	0.3798	0.2208	0.5618
2018	1.0380	0.3787	0.2732	0.5633
2019	1.2793	0.4335	0.5726	0.7618
2020	1.0112	0.3339	0.3538	0.5663
2021	1.6229	1.0051	1.0148	1.2142
Average	0.8051	0.4100	0.3801	0.5317
Growth Rate (%)	0.1854	0.1148	0.1671	0.1474
95% confidence interval	(−12.11, 80.72)	(−30.12, 90.03)	(−28.20, 97.50)	(−8.61, 64.23)

, the overall level of ecological efficiency in cryosphere tourism is generally low, exhibiting a fluctuating growth trend during the study period. The scores of each province ranged from 0.2208 to 1.6229, with the overall mean score ranging from 0.2428 to 1.2142. Additionally, the average rate of change was calculated to be 14.74%, with a corresponding confidence interval of (−8.61, 64.23). These findings indicate that the eco-efficiency of cryosphere tourism remains at a relatively low level, with annual increases accompanied by fluctuations. For individual provinces, except in 2014, the mean values for Xinjiang were consistently higher than the cryosphere average. Its mean scores ranged from 0.2418 to 1.6229, surpassing the overall mean of the cryosphere and the mean scores of other provinces. In contrast, Tibet’s mean values were mostly below the cryosphere average. Specifically, the mean scores for Qinghai and Tibet ranged from 0.2389 to 1.0051 and from 0.2208 to 1.0148, respectively. Tibet consistently lagged behind other provinces and the overall mean during the study period, indicating significant regional disparities in cryosphere TEE. In 2020, the mean scores of each province showed a significant downward trend compared to the previous year. The reasons for this can be attributed to the global pandemic, which severely impacted the tourism industry, thereby affecting TEE. This scenario is similar to the decline in TEE caused by the SARS epidemic in 2003 [24]. It is noteworthy that after excluding the impact of the pandemic, the mean scores of each province rebounded rapidly, with the overall mean score reaching 0.7302, close to the highest mean score of 0.7430 in 2019. This indicates that as the economy has returned to normal operation, the tourism economy has rebounded against the trend, and TEE is gradually improving, with the potential for further enhancement.

3.3. Tourism Ecological Efficiency Dynamic Change and Efficiency Decomposition

To depict the dynamic trend of TEE and its decomposition comprehensively and accurately, this study adopts the ML index for the analysis of the dynamic change and efficiency decomposition of cryosphere TEE from 2013 to 2021, following the research framework proposed by Heze et al. [54].

3.3.1. Dynamic Change Analysis

Overall, the ML index has a mean value of 1.2154, indicating a growing trend in cryosphere TEE with an average annual growth rate of 21.54%. This growth momentum can be attributed to the release of a series of national outlines and guiding opinions to promote the tourism industry, as well as the “13th Five-Year Plan for Tourism Development” issued by various local governments. These initiatives not only encourage the development and integration of tourism resources to enhance the economic benefits of the tourism industry but also emphasize the ecological and environmental protection of the tourism industry, thereby making tourism development more resource-efficient and environmentally friendly. As shown in

Table 5. Change and decomposition of TFP of cryosphere tourism industry.

Year	ML	PET	SE	TC	Average
2013–2014	1.0130	1.1776	1.0047	0.8915	1.0217
2014–2015	1.0820	0.9538	1.0015	1.1517	1.0472
2015–2016	1.1168	1.0208	0.9305	1.1831	1.0628
2016–2017	0.9958	0.9905	1.0046	0.9963	0.9968
2017–2018	1.1230	1.0028	0.8440	1.5490	1.1297
2018–2019	1.6378	0.9064	1.4820	1.1721	1.2996
2019–2020	0.8973	0.7304	1.5809	0.9546	1.0408
2020–2021	1.8578	2.0222	0.7560	1.7599	1.5990
Average	1.2154	1.1006	1.0755	1.2073	1.1497

, from 2013 to 2017, the ML index remained relatively stable, indicating a steady growth in TEE. However, from 2019 to 2020, the ML index significantly decreased to 0.8973, indicating a 10.27% year-on-year efficiency decline. From 2020 to 2021, the index rebounded rapidly to 1.8578, indicating an 85.78% increase in TEE in the three provinces of the cryosphere, demonstrating strong industry resilience. For individual provinces, as depicted in Figure 4, the mean ML index values for Xinjiang, Qinghai, and Tibet were 1.1854, 1.1702, and 1.2907, respectively, indicating improvements in tourism ecological dynamic efficiency, albeit varying in degree. Among them, Tibet showed the most significant improvement. The reason for this lies in the notable achievements of Tibet’s “13th Five-Year Plan for Tourism Development,” which emphasized the creation of cultural tourism products, expanding the scale of the tourism industry, providing strong support for its overall efficiency growth.

3.3.2. Efficiency Decomposition Analysis

Breaking down technological efficiency into pure technical efficiency (PET) and scale efficiency (SE), this section further investigates the driving forces behind the growth of cryosphere TEE, aiming to clarify the synergistic effects of PET, SE, and technological progress (TC) on its growth.

Overall, as shown in Table 5 the mean values of PET, SE, and TC are 1.1006, 1.0755, and 1.2073, respectively. This indicates that the growth of cryosphere TEE is driven by the synergistic effects of TC, PET, and SE, and that TC is the main source of growth in TEE. Based on the factor decomposition results, we can identify four stages. (1) Stage one (2013–2014): In this stage, both PET and SE are greater than one, while TC is less than one. The synergistic effect of the three on improving TEE is not significant. (2) Stage two (2014–2018): In this stage, except for the slight decrease in 2016–2017, TC is greater than one throughout and gradually plays a more prominent role in promoting TEE. PET and SE fluctuate around one, with their roles in improving TEE remaining insignificant. (3) Stage Three (2018–2020): In this stage, SE is consistently greater than one, indicating an increasingly significant role in improving TEE. On one hand, this may be influenced by the “Rural Revitalization Strategy Plan (2018–2022)” issued by the State Council of China, which first proposed “developing distinctive rural cultural industries” and vigorously promoted the integration of culture and tourism, leading to a substantial expansion of the cryosphere tourism industry. On the other hand, with the continuous expansion of the rural tourism market, in November 2018, the Ministry of Culture and Tourism, the National Development and Reform Commission, and 17 other departments in China issued the “Guiding Opinions on Promoting the Sustainable Development of Rural Tourism,” which explicitly stated the need to promote the quality and efficiency of rural tourism and foster new drivers of agricultural and rural development. (4) Stage four (2020–2021): In this stage, influenced by the pandemic, SE significantly decreased to 0.7560. Both technological efficiency and PET were greater than one, indicating a significant synergistic effect in improving TEE. In 2020, the Ministry of Culture and Tourism of China, the National Development and Reform Commission, and ten other departments issued the “Opinions on Deepening ‘Internet Plus Tourism’ to Promote the High-Quality Development of Tourism Industry” (Wenlu Resources Development [2020] No. 81), aiming to deepen the integration of “Internet Plus Tourism” and make information technology represented by the Internet an important driving force for the development of the tourism industry, thus driving the improvement of the cryosphere towards technological advancement.

Regarding each province, as shown in Figure 5, Xinjiang and Qinghai exhibit PET, SE, and TC all greater than one, while Tibet’s PET is lower than one, with TC and SE higher than one. This indicates that Xinjiang and Qinghai have a relatively high degree of tourism intensification and are making efforts to transition from extensive to intensive or sustainable tourism development models. Comparatively, Xinjiang’s PET and SE are closer to one compared to Qinghai, suggesting that Xinjiang’s production management and scale are closer to optimal, showcasing excellence in promoting energy conservation, emission reduction, and optimizing resource allocation. However, Tibet still needs to improve in areas such as tourism environmental governance, energy conservation, emission reduction technologies, management practices, and workforce quality.

3.4. Impact Factor Analysis

3.4.1. Gray Relational Model

To further investigate the external driving factors affecting cryosphere TEE, based on the realistic scenario where cryosphere TEE is influenced by multiple factors, this study adopts the gray relational model, drawing on the research approach of Wu Yubin et al. [55], to explore its driving factors. As a statistical analysis model that examines the closeness of relationships between mother and child factors in a system, the gray relational model focuses on identifying the primary and secondary factors causing changes in the mother factor within the system. It can effectively quantify the dynamic development trend of the system and compensate for the deficiencies caused by traditional mathematical and statistical methods in system analysis [56].

3.4.2. Variable Selection and Data Description

Taking climate change as the background and based on the perspective of carbon emission constraints, while fully considering the resource endowment, policy orientation, and functional zone positioning of the three provinces in the cryosphere, and drawing extensively from existing research achievements [3,23,57,58,59], this study selects seven variables as driving factors for cryosphere TEE.

(1)

Resource Endowment: Tourism in the cryosphere is based on scarce resources such as glaciers, permafrost, and snow, which influence surface albedo and consequently affect cryospheric temperatures. Climate warming leads to thickening of the polar atmosphere, northward extension of pressure ridges, increased north–south amplitude of air currents, and consequently a rise in the frequency of extreme events. Alongside warming, the moisture content and humidity of the atmospheric boundary layer increase, while rainfall transports heat to the underlying surface of the cryosphere, altering the physical properties of snow, affecting snow and ice melt, and thereby impacting the cryospheric tourism industry [3,60]. Drawing from the research of Cai Ziyi et al. [60], this study employs the entropy weighting method to assign weights to mean temperature, precipitation, snow depth, thunderstorm days, and hailstorm days to determine weights and calculate the resource endowment.

(2)

Carbon emission structure: The carbon emission efficiency of the tourism industry considers the development efficiency of the tourism industry under the constraint of carbon emissions. The carbon emission structure is an important factor affecting the carbon emission efficiency of the tourism industry [26], with carbon emissions from tourism transportation accounting for a significant proportion of overall ecotourism carbon emissions. Drawing from the research of Cheng Jiesheng et al. [61], this study adopts the proportion of ecotourism transportation carbon emissions to total tourism industry carbon emissions as a representation of the carbon emission structure of ecotourism.

(3)

Economic development level: The economic foundation of the cryosphere region is weak, and with climate warming, glaciers are experiencing significant shrinkage, constraining the improvement of the region’s development level [3]. The macroeconomic development level of the region is closely related to the regional tourism economy; a higher macroeconomic development level indicates relatively superior consumer demand and infrastructure, which positively influences the development of the tourism industry. Therefore, this study adopts regional per capita GDP as a measure of regional economic level indicators.

(4)

Infrastructure: Infrastructure constitutes the objective conditions necessary for the smooth operation of tourism activities in the cryosphere region. Transportation, as the most important category of tourism infrastructure, plays a crucial role in the development of regional tourism industry [23]. Drawing from the research of Cai Bingbing et al. [57] and Li Zhilong et al. [23], this study utilizes road network density as a representation of infrastructure.

(5)

Environmental regulation: At the current stage in China, environmental regulations are capable of effectively curbing carbon emissions [62], thereby incentivizing tourism enterprises to innovate technologies and management methods, enabling the tourism industry to achieve economic benefits while reducing environmental pollution from tourism [63]. Drawing from the research of Liu Rongzeng et al. [64], this study employs the ratio of investment in industrial pollution control (in ten thousand RMB) to the added value of the secondary industry (in hundred million RMB) as a measure of the intensity of environmental regulation.

(6)

Technological investment intensity: The intensity of technological investment reflects the degree of emphasis a region places on technology. The application of regional technological innovation and progress in the tourism industry not only enhances the efficiency of tourism energy resource utilization but also strengthens the energy-saving and emission reduction capabilities of tourism enterprises [57,58]. Drawing from the research of Cai Bingbing et al. [57], this study utilizes the proportion of technology expenditure to total fiscal expenditure as a measure of technological investment intensity.

(7)

Industrial structure: The tourism industry is the core component of tourism economic development and serves as the fundamental driver for regional tourism economic growth, driving tourism economic development by increasing regional tourism revenue. The growth effect of tourism is closely related to the level of tourism economic development [65]. An increase in the proportion of the tourism industry contributes to the reduction of energy consumption and carbon emission pollution, thereby affecting the efficiency of the tourism industry under carbon emission constraints [66]. Therefore, drawing from the research of Tian Hong et al. [59], this study utilizes the proportion of tourism revenue to GDP as a representation of the industrial structure.

3.4.3. Results Analysis of Gray Relational

Table 6. Correlation of influencing factors.

Province	Resource Endowment	Carbon Emission Structure	Economic Development Level	Infrastructure	Environmental Regulation	Technological Investment Intensity	Industrial Structure
Xinjiang	0.6271	0.6789	0.6751	0.6751	0.5997	0.6782	0.7210
Qinghai	0.7308	0.8152	0.8123	0.7953	0.7226	0.7944	0.6996
Tibet	0.7890	0.8454	0.8165	0.8315	0.6840	0.8027	0.8050
Average	0.7156	0.7798	0.7680	0.7673	0.6688	0.7584	0.7419

displays the gray relational degrees of the driving factors for TEE in three provinces within the cryosphere from 2013 to 2021. Regarding the mean gray relational degree of each driving factor, the mean value of resource endowment is 0.7156, indicating a strong correlation; the mean value of the carbon emission structure is 0.7798, also indicating a strong correlation; the mean value of the economic development level is 0.7680, denoting strong correlation; the mean value of infrastructure is 0.7673, also indicating strong correlation; the mean value of environmental regulation is 0.6688, suggesting a moderate correlation; the mean value of technological investment intensity is 0.7584, showing strong correlation; the mean value of industrial structure is 0.7419, indicating strong correlation. Thus, it can be observed that each driving factor is closely related to TEE.

The degree of influence of each driving factor on TEE, from largest to smallest, is as follows: carbon emission structure, economic development level, infrastructure, technological investment intensity, industrial structure, resource endowment, and environmental regulation. The highest average score in gray relational degree is obtained by the carbon emission structure, indicating its strongest driving effect on TEE. This is because carbon emissions from tourism transportation constitute the primary source of carbon emissions in the tourism industry. Reducing carbon emissions from tourism transportation is key to controlling carbon emissions and enhancing TEE in the cryosphere. Therefore, efforts should be directed towards implementing energy-saving and emission reduction measures in these key areas.

The economic development level ranks second, following closely behind the carbon emission structure, and has become a fundamental driver of TEE. On one hand, economic growth in the tourism industry can provide financial support for the advancement of low-carbon tourism technology and talent introduction, effectively improving carbon emission efficiency under constraints and enhancing TEE [66,67]. On the other hand, the potential for economic development is the material basis for the sustainable development of human ecological systems and an important guarantee for continuous spatial exchange of resource elements with the outside world, effectively reducing the vulnerability of ecosystems.

Infrastructure ranks third in terms of its influence on TEE. Improvements in transportation conditions facilitate the development of the tourism industry. However, tourism transportation is the main source of carbon emissions pollution generated by the tourism industry, posing a significant threat to the ecological environment, and leading to a decline in TEE under carbon emission constraints [66]. Therefore, provinces should focus on the coordination of supply and demand for necessary infrastructure and environmental elements, comprehensively promote the construction of new infrastructure, and consolidate the underlying support for economic “dual circulation”.

Technological investment intensity ranks fourth. The application of regional technological innovation and progress in the tourism industry enhances the efficiency of tourism energy resource utilization and strengthens the energy-saving and emission reduction capabilities of tourism enterprises, thereby improving TEE [58]. However, limited research funding may not directly target tourism environmental protection facilities and equipment [28]. Therefore, governments at all levels in the cryosphere should pay attention to and encourage the development of tourism platforms and system tools with comprehensive functions such as intelligent recommendations, decision-making, and payments for tourists.

Then, the industrial structure ranks fifth. A reasonable industrial structure is key to the ecological development of the tourism economy. This requires the tourism industry in the cryosphere to effectively promote the spillover of capital, technology, and talent within the region through agglomeration effects, to some extent alleviating the environmental issues caused by the development of the tourism industry.

The sixth ranking of influence is the resource endowment. Tourism resources are a combination of various natural and scenic elements on which people rely for tourism demand. Under the background of climate change, the reduction in natural resources in the cryosphere [60] due to rising temperatures, increased precipitation, and frequent extreme weather events has led to a decline in TEE. Therefore, provinces in the cryosphere should strengthen regional monitoring capabilities to understand trends and mechanisms of climate system changes, strictly control environmental quality, control ecological degradation from the source, and promote the construction of green tourism in cryosphere tourist destinations.

Finally, environmental regulation ranks seventh, with a score of only 0.6688, indicating a moderate level of correlation. The reason for this may be that this end-of-pipe pollution control method has not significantly improved ecological efficiency and has consumed a large amount of resources. China’s environmental protection strategy and policy evolution over the past 20 years can be divided into two stages: the environmentally friendly strategy (2001–2012), focusing on controlling total pollutant emissions and promoting ecological environment demonstration; and the ecological civilization strategy (since 2013), promoting environmental quality improvement and “Beautiful China” construction, indicating a gradual tightening of China’s environmental protection policies. Therefore, provinces in the cryosphere should identify pollution sources, improve the investment structure of environmental governance, implement precise governance, and achieve waste reduction at the source.

For each province, as shown in Figure 6, the average score of resource endowment correlation degree is 0.7156, with a pattern of Tibet > Qinghai > Xinjiang. Only Xinjiang falls below the average, indicating that with the increasingly evident climate change, the impact of reduced resource endowment in the Tibet region on TEE is more pronounced. The reason behind this lies in the fact that glaciers in Tibet cover approximately 48% of the total glacier area in China, making the ice and snow elements denser compared to those in other provinces. However, glacier retreat has led to the melting of the Kuojiong Glacier located in the north of Lhasa. In order to protect the Gangbu Glacier, the Tourism Development Bureau of Langkazi County announced in 2021 on the China Tibet Network that all tourism activities are prohibited on the Gangbu Glacier “except for scientific research, resource surveys, and other necessary work”. Consequently, this significantly affects TEE. The average scores for carbon emission structure and infrastructure correlation degrees are 0.7798 and 0.7673, respectively, showing a pattern of Tibet > Qinghai > Xinjiang. This indicates that the carbon emission structure and infrastructure play important roles in TEE in Tibet and Qinghai. Since the 18th National Congress of the Communist Party of China, Tibet’s transportation has entered a period of rapid development, while Qinghai has launched “transportation + tourism” products and continues to increase investment in tourism transportation infrastructure. Therefore, tourism transportation has become an important factor in TEE in Qinghai and Tibet. The average score for economic development level correlation degree is 0.7680, showing a pattern of Tibet > Qinghai > Xinjiang. However, there is not much difference in the impact between Qinghai and Tibet. Skiing, as a major winter sport and winter tourism activity, can create significant business opportunities and promote regional economic development [68]. According to the “2022–2023 China Ski Industry White Paper”, the area of ski trails is an important dimension for measuring the size of ski resorts. Xinjiang ranks first in the country, indicating that the Xinjiang skiing tourism market has become saturated. Therefore, the correlation between economic development level and TEE in Xinjiang is not high. The average score for the environmental regulation correlation degree is 0.6688, showing a pattern of Qinghai > Tibet > Xinjiang. This indicates that environmental regulation is gradually becoming the core driving force to overcome obstacles and bottlenecks in cryosphere TEE. It requires further strengthening of ecological environment institutional construction and enforcement standardization. The average score for technological investment intensity correlation degree is 0.7584, showing a pattern of Tibet > Qinghai > Xinjiang. The gap between Qinghai and Tibet is small, indicating that technological expenditure can stimulate innovation vitality and release technology diffusion and sharing effects, which helps to improve TEE. The average score for industrial structure correlation degree is 0.7419, showing a pattern of Tibet > Xinjiang > Qinghai. However, the impact of industrial structure in Xinjiang is the highest, indicating that the agglomeration effect of Xinjiang’s tourism industry is the key driving factor for improving TEE.

4. Discussion and Conclusions

4.1. Discussion

In this study, the average tourism eco-efficiency of the three provinces in the cryosphere is 0.5317, which is lower than the average of 0.704 in the Yangtze River basin [63]. And this study systematically analyzes the dynamic changes, efficiency decomposition, and driving factors of TEE in the three provinces of the cryosphere. It demonstrates the degree of coordination and the level of sustainable development of the human–earth system in the cryosphere, aiming to further explore the interaction between tourism industry development and ecological environment protection in Xinjiang, Qinghai, and Tibet. The objective is to optimize the tourism industry layout in the cryosphere, fill in the “shortcomings” of the tourism industry in the region, ensure the rational development of resources, and provide a realistic basis for the coordinated, balanced, healthy, and sustainable development of the tourism industry in the cryosphere.

Firstly, relying on scarce resources such as glaciers, frozen soil, and snow as basic elements, the tourism industry in the cryosphere urgently needs to implement a diversified strategy to extend the service life of resources [69]. It is essential to vigorously promote tourism activities that are suitable for the climate and terrain, reduce excessive dependence on single tourism resources, and implement diversified and innovative measures based on their own resource endowments. Secondly, technological innovation is key to improving TEE. Against the backdrop of climate change, the synergy between the economy and the environment must be emphasized. Provinces in the cryosphere should formulate sustainable development strategies tailored to their local conditions, identify strengths and weaknesses, establish long-term synergy mechanisms, strengthen the leading role of technological innovation, and fully unleash effective tourism demand. For instance, concepts such as smart cities could be leveraged, considering different historical developments that have led to the emergence of intelligent plateau decision-making tools [70]. Lastly, attention must be paid to the driving role of factors with high correlation on TEE in the cryosphere. Improving TEE in the cryosphere should not only focus on stimulating endogenous forces but also fully utilize the driving role of external factors. It is necessary to leverage advantages industries, actively guide disadvantage industries, encourage public–private cooperation models, optimize cultural and tourism experiences through the involvement of local residents, ensure respect for local culture and ecology in tourism activities, and promote coordinated development of cultural tourism and ecological protection.

In terms of limitations and areas for improvement, this study requires further refinement. Future researchers are encouraged to obtain more detailed and diverse data, including county-level, township-level, and even scenic area-level data, and to include ice and snow tourism revenue and visitor numbers as output indicators. If possible, future studies should expand the research area to cover more regions worldwide with similar cryospheric characteristics. Dividing regions based on tourist attractions or altitude will provide a more comprehensive understanding of the eco-efficiency of cryospheric tourism.

4.2. Conclusions

This study, first and foremost, contextualizes climate change and employs a carbon emission constraint perspective. It integrates multidimensional factors such as “climate change, carbon emission constraints, and cryosphere resources” into a unified measurement framework to construct a model for assessing the ecological efficiency of cryosphere tourism. Through the utilization of the super-efficiency SBM model, the ecological tourism efficiency of three provinces within the cryosphere is measured. Subsequently, the Malmquist–Luenberger index method is employed to explore the dynamic changes and efficiency decomposition of ecological tourism efficiency in these provinces. Finally, the gray relational model is utilized to identify external driving factors that enhance the ecological efficiency of cryosphere tourism. This approach helps to break the path dependence of low ecological tourism efficiency in the three provinces of the cryosphere and promotes a high-level transition of ecological efficiency in cryospheric tourism. The conclusions drawn are as follows:

(1)

The overall mean of TEE in the cryosphere is between 0.2428 and 1.2142. Over the study period, the average annual growth rate and corresponding confidence interval were 14.74%, (−8.61%, 64.23%). Over the study period, there was an average annual growth rate of 14.74%, exhibiting a significant fluctuating growth trend. Throughout the study period, the mean scores in Xinjiang ranged from 0.2418 to 1.6229, surpassing the overall mean of the cryosphere and the mean scores of other provinces. In contrast, Qinghai and Tibet had mean scores below the cryosphere average in most years, with Tibet consistently lagging behind other provinces and the overall mean, indicating significant regional disparities in the ecological efficiency of cryosphere tourism.

(2)

The dynamic efficiency of ecological tourism in the cryosphere generally shows an increasing trend, driven by the synergistic effects of TC, PET, and SE, TC is the main source of growth in TEE. While all provinces witness improvements in dynamic ecological tourism efficiency, the degree of improvement varies, with Tibet showing the most significant progress. Qinghai and Xinjiang have relatively high levels of tourism intensification and are striving to transition from extensive to intensive or sustainable tourism development models. However, Tibet still needs to improve its tourism environmental governance, energy conservation, emission reduction technologies, management practices, and workforce quality.

(3)

Regarding the driving factors of ecological efficiency in cryosphere tourism, each factor is closely related to ecological tourism efficiency, with varying degrees of influence. The driving factors in descending order of their impact on ecological tourism efficiency are carbon emission structure, economic development level, infrastructure, technological input intensity, industrial structure, resource endowment, and environmental regulation. This indicates that the carbon emission structure has the strongest driving effect on ecological tourism efficiency, while economic development level serves as the fundamental driving force. However, the degree of environmental regulation in the cryosphere is not high, leading to environmental regulations having less significant driving effects on ecological tourism efficiency compared to the other six factors.

This study offers valuable insights; however, the reliability and generalizability of the conclusions require further validation due to the lack of detailed micro-level data and long-term observational data. Additionally, the output variables in the evaluation index system did not consider the overall development indicators of the cryosphere tourism industry. Moreover, this research primarily focuses on the development of tourism in China’s cryosphere regions.