Impact of High-Speed Rail on the Development Efficiency of Low-Carbon Tourism: A Case Study of an Agglomeration in China

Abstract

As an important indicator for measuring the development level of low-carbon tourism, reducing the carbon emissions of tourism transportation has become an essential strategic goal and task for the sustainable development of tourism. Among many tourism vehicles, high-speed rails have a significant role in reducing the carbon emissions of tourism transportation. To clarify the impact of high-speed rails on the development efficiency of low-carbon tourism, using the relevant data of Zhengzhou urban agglomeration from 2010 to 2020, the DEA-BCC model and the Malmquist index method were used to measure these data. The results show the following: (1) the average comprehensive development efficiency of the Zhengzhou metropolitan high-speed rail for low-carbon tourism is low, and the comprehensive development efficiency of each city varies greatly; (2) the impact of high-speed rails on the development efficiency of low-carbon tourism in some underdeveloped areas is increasing. The impact on the development efficiency of low-carbon tourism in more developed areas is declining; (3) affected by COVID-19, tourism carbon emissions have shown a downward trend, reflecting the importance of low-carbon travel to low-carbon tourism to a certain extent. The research results not only verify the existing research conclusions but also verify the role of high-speed rails in the development of low-carbon tourism, and have practical value with respect to targeted guidance for the development of low-carbon tourism.

1. Introduction

The deterioration of the ecological environment and climate means that climate change is a priority problem for humanity [1]. The global development of a low-carbon economy has become an inevitable requirement [2]. Various countries have respond to the need for developing a low-carbon economy and have aspired to achieve the double carbon targets of carbon neutrality and emission peak [3]. This is an essential direction for the development of human society and a meaningful method to optimize industrial structure, realize industrial upgrading and green growth, and take essential steps to achieve higher socioeconomic and environmental benefits [4,5]. In the context of a low-carbon economies, low-carbon tourism has been proposed as a new sustainable development concept [6]. To improve the tourism experience, and maximize tourism benefits, the use of low-carbon technologies, the implementation of carbon sink mechanisms, and the promotion of low-carbon tourism consumption patterns have been combined to bring about sustainable tourism development [7]. Although tourism has a reputation as a “smokeless industry” and scenic spots in themselves are naturally consistent with low-carbon development goals, some aspects of tourism activities continue to impact the environment [8]. Since the carbon emissions of tourism transportation account for the largest proportion of the total carbon emissions of tourism, the impact of tourism transportation on the environment is the most prominent [9]. Reducing the carbon emissions of tourism transportation could significantly promote the development of low-carbon tourism. Therefore, studying the impact of transportation on the development efficiency of low-carbon tourism is important for the sustainable development of tourism and the promotion of a low-carbon lifestyle. Among the many forms of tourism transportation, high-speed rails have the characteristics of strong accessibility, safety and reliability, saving energy, and environmental protection [10,11,12]. The time-space compression effect they produce can improve accessibility effectively in a region, which helps to increase the frequency of travel [13]. At the same time, they can optimize energy consumption patterns, improve energy intensity, and effectively reduce carbon emissions [14].

Zhengzhou is located in the middle of mainland China. Zhengzhou is one of China’s nine major national central cities (among several others). Compared with Beijing and Shanghai, it is not only a famous historical and cultural city in China but also an essential comprehensive transportation hub in China. In terms of tourism resources, Zhengzhou is an important birthplace of Chinese civilization, and is also the ancient capital of the Five Dynasties, making it rich in historical resources [15]. Among these resources, Shaolin Temple, Longmen Grottoes, and Qingming Shanghe Garden are world-renowned [16]. Regarding transportation resources, the high-speed rail network centered in Zhengzhou is closely connected with China’s high-speed rail network. It has an important strategic position to connect both the north and the south and the east and west. According to the Statistical Bulletin of China’s National Economic and Social Development, Zhengzhou achieved a total tourism revenue of 159.86 billion yuan in 2019, a year-on-year increase of 15.2%, while Beijing and Shanghai increased by 5.1% and 5.2%, respectively, over the same period. At the same time, the bulletin showed that in 2019, the passenger volume of the Zhengzhou railway reached 70.234 million, a year-on-year increase of 8.2%, while Beijing and Shanghai increased by 2.8% and 4.7%, respectively, over the same period. It can be concluded that the development of tourism in Beijing and Shanghai is relatively mature. Still, from the perspective of the growth rate of total tourism revenue and railway passenger traffic, Zhengzhou has a faster growth rate. In addition, the “14th Five-Year Plan for Tourism Development” issued by the State Council in January 2022 attaches more importance to developing tourism in Zhengzhou. The plan points out that Zhengzhou will be built into a hub city for China’s tourism in order to enhance the radiation and driving effect of regional tourism. At the same time, the plan also pointed out that Luoyang would be built into a key tourist city in China. According to China’s medium and long-term railway network planning (2016–2030), Henan Province has basically formed a high-speed railway network with Zhengzhou City as its core. This high-speed rail network not only connects the cities in the Zhengzhou urban agglomeration, but also enables high-speed rail tourism in the metropolitan group. Among them, Zhengzhou is the core city, and the other eight cities are connected to it in the high-speed rail network. At the same time, Zhengzhou urban agglomeration is connected to other urban agglomerations in China, such as the Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing and other key urban agglomerations, and it is about 500 km away from the Wuhan urban agglomeration. To sum up, the Zhengzhou region has unique tourism resources, fast-growing total tourism revenue, the importance of Chinese government policies and the developed high-speed rail network. Therefore, this paper selected the urban agglomeration in Zhengzhou as its research object.

To further explore the impact of high-speed rail on the efficiency of low-carbon tourism development, this paper takes the Zhengzhou urban agglomeration as its object, strips and selects the key indicators that high-speed rail affects (such as the development efficiency of low-carbon tourism), and constructs an input-output model of the impact of high-speed rail on low-carbon tourism efficiency. The paper identifies the main reasons for the input-output imbalance between high-speed rails and low-carbon tourism. It suggests improving the development efficiency of high-speed rails for low-carbon tourism. The research conclusion has significant academic and practical value. In terms of academic value, this paper analyzes the impact of high-speed rail on the development efficiency of low-carbon tourism in urban agglomerations. It constructs an evaluation model and an evaluation index system for evaluating the development efficiency of low-carbon tourism. These have a certain role in promoting the improvement and supplementation of related theories such as tourism transportation and low-carbon tourism. At the same time, theoretical innovation and exploration are carried out on the evaluation indicators and research areas of low-carbon tourism development under the high-speed rail network, and the research field of low-carbon tourism is expanded. In terms of practical value, by analyzing the network effect of high-speed rail in the development efficiency of low-carbon tourism, the space optimization of the development efficiency of low-carbon tourism in urban agglomerations can be realized, thereby providing a theoretical reference for tourism management departments to formulate low-carbon tourism development strategies and better develop low-carbon tourism as one of the strategic goals of high-quality tourism sustainable development, achieving low-carbon socioeconomic sustainable development.

2. Literature Review

2.1. Related Study of Low Carbon Tourism

In May 2009, the World Economic Forum first proposed the agenda “Towards Low-Carbon Travel and Tourism [17].” Since then, low-carbon tourism has gradually become the focus of academic attention [18]. When interpreting the connotations of low-carbon tourism, some scholars have proposed that low-carbon tourism has the characteristics of low energy consumption, low pollution, and low emission [19]. It is a means of reducing carbon emissions in tourist activities through technological innovation. It proposes not only new requirements for the development of tourism resources but also involves specific requirements for tourists [20]. However, Pan used the SBM-DEA model to calculate the carbon emission efficiency of China’s tourism industry from 2007 to 2017, and found that the comprehensive benefits, pure technical benefits, and scale benefits of my country’s tourism industry have not yet reached the production frontier. There is still a lot of room for improvement in the low-carbon tourism development [21]. Some studies also believe that reducing the carbon emission intensity of tourism and promoting low-carbon transportation are powerful guarantees for realizing low-carbon tourism [22]. For example, a study pointed out that in the tourism-related greenhouse gas emissions, transportation accounted for 75% of the greenhouse gas emissions [23]. It can be seen that that effectively reducing the carbon emissions of tourism transportation is of great significance to the development of low-carbon tourism. In addition, some scholars took coastal area’s low-carbon tourism environment carrying capacity as an example and established a linear programming model of environmental carrying capacity from an ecological perspective to quantify the low-carbon tourism environment [24]. Some scholars studied the importance of multi-dimensional views of tourism and tourists in developing low-carbon tourism and formulated development plans according to specific trends [17]. According to the survey, the current development of low-carbon tourism faces difficulties such as improper utilization of resources, lack of environmental protection awareness, and damage to the development of tourism resources and the environment [25]. It results in an overall low level of low-carbon tourism development and significant variations in the developmental efficiency of low-carbon tourism in different regions [9]. This requires researchers and the tourism industry not only to strengthen the research on basic knowledge of low-carbon tourism but also to evaluate the suitability of low-carbon tourism development [26]. It is also necessary to analyze the development process of low-carbon tourism with broader thinking, a more prosperous path, and a more sustainable development concept [27].

Low-carbon tourism will play a positive role in developing sustainable tourism [28]. It can achieve a sustainable tourism transition [29]. At the same time, low-carbon tourism will have significant value and hold significance for both ecological environmental protection and socioeconomic development [30]. Therefore, as an essential link in the realization of low-carbon tourism, reducing tourism carbon emissions is also an important task and challenge to achieve sustainable tourism development [4,31]. However, on a global scale, tourism has not shown a sustainable development trend [32]. To this end, scholars have developed a series of new methods and models, such as sustainable tourism evaluation models [33], preference expectations for sustainable tourism [34], and realization of sustainable tourism competitiveness [35] to achieve sustainable tourism development.

2.2. Research on Low Carbon Tourism Development

The development of transport is closely linked to the development of tourism. Without transportation, there would be no travel and tourism [9,36]. In progressive research, scholars have shown that transportation and low-carbon tourism are the keys to achieving sustainable socioeconomic development [37,38]. Therefore, research on the influencing factors of low-carbon tourism development has gradually shifted to the transportation aspect [26]. According to the survey, the energy consumption of transportation, accommodation, and activities in the tourism industry accounts for 94%, 4%, and 2%, respectively [39]. It can be found that due to the highest proportion of tourism transportation energy consumption, the proportion of tourism transportation carbon emissions in the total tourism carbon emissions is also the highest [9]. Scholars analyzed tourists’ willingness and behavior through questionnaires and found that transportation is a crucial factor affecting low-carbon tourism development [40,41]. At the same time, scholars have constructed a dynamic model between tourism, transportation, and carbon emissions and found that there is a specific relationship between the three (which has guiding significance for sustainable tourism development) [38]. Scholars used the SBM-Undesirable model to measure and analyze the low-carbon efficiency of tourism and its total factor productivity in Hubei Province from 2007 to 2011. They found that the low-carbon efficiency of tourism in Hubei Province was generally at a low level but showed an upward trend (and that there were regions in each city). At the same time, the transportation sector accounts for the highest proportion of energy consumption and carbon dioxide emissions and is increasing year by year [42]. Using spatial and temporal difference factors, Du Peng constructed a model for calculating the carbon footprint of China’s tourism transportation and found that increasing the proportion of low-carbon transportation and improving energy efficiency are the main directions for China’s low-carbon travel [43].

At present, as the problem of coordinated development between transportation and the environment becomes more and more prominent, low-carbon travel has become a hot spot of concern at home and abroad [44]. Some studies have shown that after high-speed rails come into operation, they can effectively relieve the pressure of road transportation and significantly reduce transportation pollution [45,46]. Scholars have used the multi-cycle differential service model to calculate and found that high-speed rails have less carbon emissions during operation among many transportation modes. At the same time, opening high-speed rails in a city will reduce the city’s carbon emissions by about 2.4% on average, which has the characteristics of energy saving and emission reduction [14,47]. At the same time, China’s high-speed rails mainly achieve a low-carbon effect through alternative models, attracting air passengers to take high-speed rail, saving 12 million tons of carbon emissions per year for the environment [48,49]. In short, high-speed rails have a crucial impact on developing low-carbon tourism. In addition, high-speed rails and tourism are two closely related economic activities. With the continuous improvement of high-speed rail technology and management services and the continuous improvement of the high-speed rail networks, more tourists tend to choose high-speed rail as their main means of travel [50], thereby improving the mobility of tourists and promoting a change in tourism behavior [51]. In addition to providing tourists with safe, comfortable, and convenient transportation conditions, high-speed rails reduce travel time. It prolongs the leisure time of tourists in tourist destinations, thereby achieving high-quality tourism [13,52].

To sum up, many scholars have carried out comprehensive research on low-carbon tourism. They systematically analyzed the concept, characteristics, development mode, development problem, and development significance of low-carbon tourism. Research shows that low-carbon tourism is not only an inevitable trend in developing sustainable tourism but also an important way to achieve sustainable social and economic development. At the same time, scholars have found that transportation is a critical factor in the development of low-carbon tourism. As a means of transportation using electricity as energy, high-speed rails have distinct advantages such as saving energy, environmental protection, safety and comfort, high efficiency, and no congestion. However, few papers have discussed the development efficiency of low-carbon tourism in the context of high-speed rails. Therefore, to explore new ideas for sustainable tourism development, this paper takes the Zhengzhou urban agglomeration as an example and uses the DEA model. This research takes high-speed rail as an input variable and low-carbon tourism as an output variable to discuss the impact of high-speed rail on the development efficiency of low-carbon tourism. The aim is to provide a theoretical reference for the development of low-carbon tourism in tourism-related departments.

3. Research Design

3.1. DEA Method

The main methods of efficiency measurement are divided into two types according to whether the production function is known or not. These methods are the stochastic production frontier method (SFA) and the data envelopment analysis method (DEA) [53]. In recent years, many studies have evaluated the development efficiency of tourism-related industries by using the SFA model [54,55,56,57]. Based on panel data, the impact of random factors on output has been studied under the assumption of the specific form of the production function and the distribution of technical inefficiency items. The development efficiency of tourism-related industries is deeply and comprehensively understood. However, expanding the basic assumptions of the SFA model is difficult, resulting in strict requirements for input and output data and potential calculation failure. In addition, the SFA model cannot handle the multi-output situation, making it necessary to incorporate the multi-output into a comprehensive output. The processing method is therefore relatively complicated. Currently, the DEA method is considered one of the most commonly used methods in the tourism industry [58]. The DEA model is a linear programming method used to evaluate the development efficiency of a decision-making unit with multiple inputs and outputs [59]. It is unnecessary to specify the production of inputs and outputs when using this method. It is not required to convert various indicators into the same monetary unit, but only to convert multiple inputs and outputs into efficiency ratios [60]. Therefore, the DEA model is more suitable for studying the impact of high-speed rail on the efficiency of low-carbon tourism development. Whether the returns to scale change, the DEA model is divided into two forms, the BCC model (variable returns to scale, VRS) and the CCR model (constant returns to scale, CRS). The BCC model assumes variable returns to scale and decomposes technical efficiency into pure technical efficiency and scale efficiency [61]. The government and enterprises cannot determine and control the output level of low-carbon tourism. In practice, the output of low-carbon tourism will be affected by many factors that satisfy the VRS assumption. Therefore, this paper adopts the DEA-BCC model to study input and output relationships.

Supposing there are n decision-making units, and each decision-making unit has m kinds of input variables x1j, x2j, ..., xmj and s kinds of output variables y1j, y2j, ..., ysj (where xij > 0; yrj > 0; I = 1, 2, …, m; r = 1, 2, …, s; j = 1, 2, …, n), and λj is the weight vector between the input and output of each decision-making unit. In the input-oriented BCC model, each decision-making unit DMUj has its corresponding efficiency evaluation index θ. For the optimal model of the input-output efficiency of the decision-making unit DMUj0, see Formula (1) [62,63]:

$$\left\{\begin{array}{c}min \theta \\ s.t.{\displaystyle {\displaystyle \sum }_{j=1}^n}{\lambda }_j{y}_j\le \theta x_0\\ {\displaystyle {\displaystyle \sum }_{j=1}^n}{\lambda }_j{y}_j\ge y_0\\ {\displaystyle {\displaystyle \sum }_{j=1}^n}{\lambda }_j=1\end{array}\right.$$

(1)

In Formula (1), ${\lambda }_j\ge 0$, j = 1, 2, ..., n. In the DEA model, the relative efficiency value is located at [0, 1], and the relative efficiency value of 1 is regarded as an effective combination.

3.2. Malmquist Index Analysis

The DEA-BCC model can effectively analyze the utilization of various production factors in the low-carbon tourism industry, but this model has some shortcomings. The DEA-BCC model produces unpredictable results when the time dimension changes. The Malmquist index model can reflect the changes in production efficiency in different periods [64,65], which can effectively complement the DEA-BCC model. Therefore, this paper uses the Malmquist index method to study the changing characteristics of low-carbon tourism efficiency in the Zhengzhou urban agglomeration. The Malmquist index model is shown in Formula (2) [66]:

TFP_ch = TE_ch × T_ch = PTE_ch × SE_ch × T_ch

(2)

In Formula (2), TFP_ch represents the Malmquist productivity index, which shows the changes in the level of total factor productivity. When TFP_ch is greater than 1, it indicates that the level of total factor productivity increases, and vice versa. TE_ch represents the technical efficiency change index, which shows the change in production efficiency under the condition of a specific technical level. When TE_ch is greater than 1, it indicates that the production efficiency is improved, and vice versa. T_ch represents the technological progress change index, the degree of change in the production technology during the test period. When T_ch is greater than 1, it represents technological progress; otherwise, the technological level remains unchanged or regresses. PTE_ch represents the pure technical efficiency change index, the change in technical efficiency under the condition of constant return to scale. If PTE_ch is greater than 1, it indicates that the technical efficiency is improved; otherwise, the technical efficiency is decreased. SE_ch represents the scale efficiency change index, which means the impact of scale on production efficiency under the condition that the technical level and technical operation efficiency remain unchanged. If SE_ch is greater than 1, it indicates scale optimization; otherwise, it indicates scale deterioration [67].

3.3. Selection of Research Objects and Indicators

Most metropolitan areas are centered on one or more central cities, including multiple surrounding municipal-level regions or even county-level regions, which is not conducive to comparing the differences in the efficiency of high-speed rail in different cities in the development of low-carbon tourism. To clearly compare the differences in the development efficiency of low-carbon tourism by high-speed rail in different cities, this paper innovatively takes urban agglomerations as the research object. According to the previous analysis, the research scope of this paper includes all high-speed rail and low-carbon tourism development in Zhengzhou City, Kaifeng City, Xinxiang City, Jiaozuo City, Xuchang City, Luohe City, Shangqiu City, Hebi City, and Luoyang City. The basic situation of each city in the Zhengzhou urban agglomeration will be explained below from the perspective of travel resources, the number of tourists, and the construction of high-speed rail. First, travel resources. Zhengzhou urban agglomeration is rich in tourism, including rich historical and cultural resources and unique natural landscape resources, such as Longmen Grottoes in Luoyang, Qingming Shanghe Garden in Kaifeng, Yuntai Mountain in Jiaozuo, etc. We include the variable of tourism resource endowment, measured according to China’s national standards of qualified tourism resources “China’s Standards of Ratings for Tourist Attractions’ Quality.” This national standard assesses all of the domestic scenic spots according to their performance in sightseeing, safety, hygiene, postal and telecommunications services, commercial services, and environmental protection; the above-standard scenic spots are rated as levels of AAAAA (5A), AAAA (4A), AAA (3A), AA (2A), and A (from the highest standard to a merely adequate standard). Therefore, this study measured the tourism resource endowment as the number of scenic spots fitting the national standards (covering grades from A to AAAAA). According to statistics, by the beginning of 2021, there are 58 3A, 4A, and 5A scenic spots in Luoyang, 39 in Zhengzhou, 26 in Xuchang, 20 in Jiaozuo, and 20 in Xinxiang. Among the 15 5A-level scenic spots in 17 prefecture-level cities in Henan Province, 9 prefecture-level cities in the Zhengzhou urban agglomeration occupy 9 5A-level scenic spots, accounting for 60%. Second is the number of tourists. From 2010 to 2020, the number of tourists in Zhengzhou urban agglomeration increased rapidly. Among them, the number of tourists in Zhengzhou increased from 48.322 million in 2010 to 130.595 million in 2019, an increase of about 2.7 times. The number of tourists in Luoyang increased from 60.79 million in 2010 to 142 million in 2019, an increase of about 2.3 times. In 2020, the number of tourists had dropped significantly due to the impact of COVID-19. Among them, the number of tourists in Luoyang dropped significantly, reaching 92.953 million, a decrease of 35%. Third is the construction of high-speed rail. Since 2010, the Zhengzhou urban agglomeration has accelerated the construction of the high-speed rail network. In 2010, there were five high-speed rail platforms in nine cities. In 2012, it increased to 30 platforms, and in 2016, it increased to 61 platforms. In 2020, a total of 71 platforms were put into use. It should be noted that as of the article’s submission time, the government has not made public the relevant indicator data for 2021. Therefore, this paper uses panel data for various indicators from 2010 to 2020.

In the selection of input indicators, since the fiscal expenditure is crucial in the development of high-speed rail, there are no specific statistics on fiscal expenditure on high-speed rail. Therefore, referring to relevant research results and using relevant variables as approximations for research, the transportation financial advance cost is selected as the input index [68,69,70]. The increase in the number of high-speed railway stations promotes the expansion of the scale of the high-speed railway, and the size of the high-speed railway is an essential factor in measuring the development of low-carbon tourism. Therefore, the number of high-speed railway stations is selected as an input indicator. The input indicators in this paper include the number of high-speed rail stations (number of platforms) and the transportation financial advance expense (100 million yuan). In the selection of output indicators, the total tourism income and the total number of tourists reflect the regional tourism development status [71]. Tourism carbon emissions directly reflect the low-carbon tourism output index [9]. Thus, the total tourism income, total number of tourists, and tourism carbon emissions are the best choices for the output indicators of low-carbon tourism development efficiency. Among them, the total tourism revenue includes international tourism (foreign exchange) and domestic tourism revenue, and the unit is 100 million yuan. The total number of tourists consists of the number of inbound tourists, the number of outbound tourists, and the number of domestic tourists. Each trip is counted as one person, and the unit is 10,000. However, there are currently no specific statistical data on tourism carbon emissions, and tourism carbon emissions need to be calculated based on total tourism revenue. The specific tourism carbon emission (TCE) calculation is shown in Formula (3) [72]:

TCE = A × G

(3)

In Formula (3), TCE (10,000 t) represents the carbon emissions of the tourism industry in the year, A (100 million yuan) represents the total revenue of the tourism industry in the year, and G represents the carbon emission intensity of the tourism industry in the year. Since China has not issued a TCE intensity standard, this paper adopts the international TCE intensity of 623.13 kg/thousand US dollars as the reference value of G. To reduce the correlation between output indicators, the selection of output indicators includes tourism carbon emissions (10,000 t) and the total number of tourists (10,000 people).

The relevant indicator data in this paper come from the years 2011 to 2021 in the Statistical Yearbook of the Zhengzhou urban agglomeration and Henan Province, the years 2010 to 2020 of the Zhengzhou urban agglomeration Economic and Social Development Statistics Bulletin, and the official website of China Railway, www.12306.cn (accessed on 29 June 2022). The indicator data sources are representative, reliable, authoritative, and available.

4. Empirical Analysis

4.1. Analysis of the Development Efficiency of Low-Carbon Tourism in the Zhengzhou Urban Agglomeration

This paper takes the Zhengzhou urban agglomeration high-speed rail and low-carbon tourism input and output indicators as its objects. It uses DEAP2.1 software to measure and analyze the comprehensive development efficiency of the Zhengzhou urban agglomeration high-speed rail on low-carbon tourism. The average value of comprehensive development efficiency in each year was used for analysis to prevent the fluctuation of comprehensive development efficiency caused by unique events in individual years.

Table 1. The comprehensive efficiency of low-carbon tourism in the Zhengzhou urban agglomeration from 2010 to 2020.

Area	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean
Zhengzhou	1.000	1.000	0.960	0.768	0.756	0.469	0.620	0.984	0.421	0.298	0.583	0.714
Kaifeng	1.000	1.000	1.000	1.000	1.000	1.000	0.987	0.790	0.978	0.761	1.000	0.956
Xinxiang	0.564	0.586	0.385	0.343	0.553	0.728	0.484	0.490	0.383	0.345	0.375	0.476
Jiaozuo	1.000	1.000	1.000	1.000	1.000	0.648	0.912	0.596	0.601	0.899	0.593	0.841
Xuchang	0.254	0.232	0.164	0.150	0.242	0.251	0.335	0.255	0.338	0.338	0.184	0.249
Luohe	0.163	0.125	0.109	0.125	0.161	0.101	0.133	0.146	0.140	0.080	0.147	0.130
Shangqiu	0.252	0.254	0.257	0.258	0.243	0.240	0.114	0.126	0.101	0.102	0.183	0.194
Hebi	0.518	0.470	0.344	0.465	0.658	0.604	1.000	0.775	0.859	0.357	0.462	0.592
Luoyang	0.944	0.920	0.876	0.648	1.000	1.000	1.000	1.000	1.000	1.000	1.000	0.944
mean	0.633	0.621	0.566	0.529	0.624	0.560	0.621	0.573	0.536	0.464	0.503	0.566

shows the results. From 2010 to 2020, the average comprehensive development efficiency of high-speed rails in the Zhengzhou urban agglomeration for low-carbon tourism was low, at only 0.566. Only the input and output of high-speed rail and low-carbon tourism in individual cities have reached a balance. From 2010 to 2020, the average spatial difference in the comprehensive development efficiency of low-carbon tourism in the Zhengzhou urban agglomeration was noticeable. Among them, Kaifeng, Jiaozuo, and Luoyang’s comprehensive efficiency exceeded 0.8, indicating good efficiency. The comprehensive efficiency of Zhengzhou and Hebi both exceeded 0.5 and were lower than 0.8, the second-best rate. The comprehensive efficiency of Xinxiang, Xuchang, Luohe, and Shangqiu was lower than 0.5, a low state. However, about 44% of the cities in the Zhengzhou urban agglomeration had low comprehensive efficiency development. The balance between high-speed rail and low-carbon tourism in the Zhengzhou urban agglomeration needs to be urgently improved.

The comprehensive efficiency is determined by scale efficiency and technical efficiency. Figure 1 divides the nine cities into four types based on the ten-year average value of pure technical efficiency and scale efficiency in the Zhengzhou urban agglomeration (taking 0.8 as the dividing line). The technical efficiency and scale efficiency of Luoyang, Kaifeng, and Jiaozuo are all over 0.8, a relatively high rate. This indicates that high-speed rail has a relatively high impact on the development efficiency of low-carbon tourism. According to the survey, the governments of Luoyang, Kaifeng, and Jiaozuo have significantly increased their financial expenditure on transportation construction, which has led to the rapid development of high-speed rail. In addition, the three cities have a long history, a rich culture, and considerable tourism resources, all factors that appeal to tourists. Xinxiang and Shangqiu are scale-efficiency-dominant cities. High-speed rails in these types of cities has high-scale efficiency in developing low-carbon tourism, but the development level of technical efficiency is low. This shows that the high-speed rail in the two cities generates a relatively high degree of satisfaction and demand for low-carbon tourism development in terms of resource factor input. However, the technical level is low and remains in the development process. Zhengzhou and Hebi are cities dominated by technical efficiency. The technical efficiency of high-speed rail for low-carbon tourism development is optimal, while the development of scale efficiency is at a low level. This shows that the existing scale of high-speed rail in the two cities is not enough to meet the needs of low-carbon tourism development. The scale efficiency and technical efficiency of Xuchang and Luohe’s high-speed railways for low-carbon tourism development are both low. Although Xuchang and Luohe opened high-speed railways in 2012, the scale construction of high-speed railways is struggling to meet the development of low-carbon tourism. In addition, the tourism development of the two cities suffers from weak development and publicity, and there are few outstanding scenic spots to attract tourists.

4.2. Dynamic Analysis of the Development Efficiency of Low-Carbon Tourism in the Zhengzhou Urban Agglomeration

The Malmquist index model measures the technical efficiency change, technological progress change, pure technical efficiency change, scale efficiency change, and total factor productivity change in the Zhengzhou urban agglomeration from 2010 to 2020. This paper measures and calculates the time series and spatial sequence.

4.2.1. Time Series

Table 2. Changes in the efficiency of low-carbon tourism in the Zhengzhou urban agglomerations from 2010 to 2020.

Year	Effch	Techch	Pech	Sech	Tfpch
2010–2011	0.952	0.900	0.939	1.015	0.857
2011–2012	0.866	1.149	0.910	0.951	0.995
2012–2013	0.969	1.095	1.027	0.944	1.061
2013–2014	1.237	0.778	1.152	1.074	0.963
2014–2015	0.879	1.107	0.958	0.917	0.972
2015–2016	1.061	0.649	1.172	0.905	0.688
2016–2017	0.944	1.521	0.944	1.000	1.436
2017–2018	0.920	1.342	0.954	0.964	1.234
2018–2019	0.826	1.147	1.069	0.772	0.947
2019–2020	1.175	0.450	0.904	1.300	0.529
mean	0.983	1.014	1.003	0.984	0.968

shows the 11-year average of the total factor productivity change of the Zhengzhou urban agglomeration high-speed rail in the development of low-carbon tourism is 0.968. This indicates that the average annual decline rate of total factor productivity is 0.32%. Among them, technological progress and pure technical efficiency increased by 1.4% and 0.3%, respectively, while technical efficiency and scale efficiency decreased by 1.7% and 1.6%, respectively. This shows that the technical efficiency level of high-speed rail showed a downward trend, and the scale efficiency of high-speed rail did not reach the optimal mode of total factor productivity growth. The total factor production change index was greater than 1 in only 3 periods during the 11 years. The overall trend shows that the low-carbon tourism productivity of the Zhengzhou urban agglomeration showed a fluctuating decline, and that the fluctuation trend was large. First, in 2020, the change in technological progress, the change in pure technical efficiency, and the change in total factor productivity are all less than 1, and the change in technological progress has the most obvious decline. However, the change in technical efficiency and the change in scale efficiency are greater than 1, showing an increasing trend. On the one hand, affected by the epidemic, the total tourism revenue of each city in the Zhengzhou urban agglomeration will decrease in 2020, and the tourism carbon emissions will all show a downward trend. This suggests that more effort and greater efficiency are not needed to solve the problem of sustainable tourism. On the other hand, the growth trend of technological efficiency changes in 2020 indicates that organizational management and industrial structure have been optimized under the existing technical conditions. The decline in technological progress and changes shows that although the technical level of high-speed rails has been improved, the overall efficiency of low-carbon tourism is not significantly improved. Second, when the total factor productivity change index is greater than 1, the technological progress change index is greater than 1. The growth rate of total factor productivity was the highest in 2016–2017, while the growth rate of technological progress also showed the fastest trend. The rapid decline in total factor productivity in 2019–2020 was accompanied by a sharp decline in technological progress. To sum up, the rapid trend shows that the technological progress of high-speed rail has the greatest impact on the development efficiency of low-carbon tourism. Third, from 2016 to 2019, the technological progress change in the Zhengzhou urban agglomeration was greater than 1, but the total factor production change rate showed a downward trend. In the 2018–2019 period, it was lower than 1. The reason for this result is related to the low change in scale efficiency. The 11-year average shows that the total factor productivity and the scale efficiency change index are less than 1. This shows that the development of technical efficiency and the construction of scale efficiency are equally important. Fourth, from 2010 to 2020, the change of pure technical efficiency showed a fluctuating growth, with an average annual growth rate of 0.03%. This indicates that the technical management level of Zhengzhou urban agglomeration high-speed rail for low-carbon tourism development has been effectively improved. However, the efficiency of the scale showed a fluctuating decline, with an average annual decline of 0.16%. This indicates that the industry scale of low-carbon tourism development in the Zhengzhou urban agglomeration has not reached an optimal model for high-speed rails.

4.2.2. Spatial Sequence

Table 3. Changes in total factor productivity and decomposition efficiency of low-carbon tourism in the Zhengzhou urban agglomeration.

Area	Effch	Techch	Pech	Sech	Tfpch
Zhengzhou	0.947	0.972	1.000	0.947	0.921
Kaifeng	1.000	0.803	1.000	1.000	0.803
Xinxiang	0.960	1.081	0.960	1.000	1.037
Jiaozuo	0.949	1.046	0.961	0.988	0.993
Xuchang	0.968	0.995	0.985	0.983	0.964
Luohe	0.989	1.012	1.086	0.911	1.002
Shangqiu	0.968	0.822	1.002	0.966	0.796
Hebi	0.988	0.969	1.000	0.988	0.958
Luoyang	1.006	0.980	1.000	1.006	0.986
mean	0.975	0.964	0.999	0.977	0.940

shows the results of changes in the efficiency of high-speed rail in the Zhengzhou urban agglomeration on low-carbon tourism development from 2010 to 2020. First, the total factor productivity change rate in two cities in the Zhengzhou urban agglomeration is greater than 1. This indicates that the development level of high-speed rails in these two cities is on the rise for low-carbon tourism. The changes in technological progress in the above two cities are all greater than 1. This indicates that technological progress plays an essential role in the changes in total factor productivity. Second, the total factor production change index in Zhengzhou, Kaifeng, Jiaozuo, Xuchang, Shangqiu, Hebi, and Luoyang was less than 1. Xuchang’s technical efficiency change, technological progress change, pure technology efficiency change, and scale efficiency change were all less than 1. This indicates that the high-speed rail in Xuchang has shown negative overall growth in low-carbon tourism development in the past 11 years. The reason may be that the high-speed rail and low-carbon tourism in Xuchang City have not been optimally developed, and the input of various elements and resources is lacking. As an essential hub of China’s transportation, Zhengzhou has advanced technological input and strong economic strength. Still, it is difficult to achieve high growth in terms of total factor productivity and scale efficiency. As a result, the total factor productivity change index in Zhengzhou is less than 1. Factor productivity changes in Hebi and Luoyang were above average. In terms of high-speed rail, these cities attach importance to technological progress and large-scale construction and have made important contributions to the progress of the Zhengzhou urban agglomeration’s high-speed rail in the development efficiency of low-carbon tourism. Finally, the technical efficiency change in the Zhengzhou urban agglomeration showed a downward trend on average. Only Kaifeng was in a balanced state; Luoyang demonstrated growth at a rate of 0.6%. This reflects the slow technological development of the Zhengzhou urban agglomeration. The scale efficiency change indices of Zhengzhou, Luohe, and Shangqiu are all lower than the average, and the utilization of resources is low. The total factor productivity change index in Zhengzhou, Kaifeng, and Shangqiu is lower than the average, which does not produce a good radiation effect on the surrounding cities.

5. Conclusions and Discussion

5.1. Research Conclusions

This paper studies the impact of high-speed rails on the development efficiency of low-carbon tourism using the DEA-BCC model and the DEA-Malmquist index model, analyzing the development status and problems of low-carbon tourism efficiency in the Zhengzhou urban agglomeration from 2010 to 2020. The main reasons for the imbalance of input and output between high-speed rail and low-carbon tourism are analyzed. The main research conclusions are as follows.

First, the average comprehensive development efficiency of low-carbon tourism in the Zhengzhou urban agglomeration is low. In general, there is a fluctuating decline, and only a few cities have a satisfactory level of comprehensive development efficiency. The reasons are as follows: (1) although the high-speed railway in the Zhengzhou urban agglomeration has developed rapidly, the increase in the total number of tourists was not evident before the opening of the high-speed railway; (2) in recent years, the number of private cars has increased sharply with the improvement of people’s living standards. More tourists choosing to travel by car will increase the carbon emissions of tourism. The tourism industry should realize the simultaneous development of scale efficiency and technical efficiency, reflect local conditions and scientific rationality in expanding scale efficiency, and avoid unnecessary resource development and scale cost waste caused by blind expansion. In terms of improving technical efficiency, enhancing the ability of scientific and technological innovation, increasing relevant talents and technical investment, attracting more tourists to choose to travel by high-speed rail, stimulating the development potential of various elements of low-carbon tourism, and realizing sustainable tourism.

Second, the comprehensive development efficiency of each city in the Zhengzhou urban agglomeration is quite different. The reason is that high-speed rails have developed rapidly in recent years, but this development started late. Although it has improved the convenience of regional transportation, high-speed rails have not fully exerted its advantages in improving urban transportation accessibility in the region. It cannot promote the coordinated development of low-carbon tourism in the region. At the same time, some cities have good high-speed rail infrastructure, but the level of tourism development is low. It is required that tourism destinations dig deep into tourism resources and conform to the trend of integration of various industries tourism industries. “Railway integration,” “cultural tourism integration,” etc., create new tourism consumption concerns according to the characteristics of each tourism destination, and give full play to resource advantages. We suggest that there is an effort made to create an exclusive brand and increase the attractiveness of tourist destinations to adapt to the rapid development of high-speed rail.

Third, from the perspective of spatial distribution, high-speed rails have shown an increasing trend in the development of low-carbon tourism in some underdeveloped regions and a downward trend in the development of low-carbon tourism in more developed regions. However, the technical efficiency and scale efficiency of low-carbon tourism industry development are relatively backward; they can directly imitate advanced technologies and scale construction in developed areas. As a result, development costs are lower, and rapid development can be achieved. In developed regions, although the technical level and scale of the construction foundation of low-carbon tourism development are relatively good, technological progress can only be achieved by relying on their own innovation, often leading to sluggish development or even decline. For underdeveloped areas, advanced technology and management experience should be actively introduced, and the initiative to undertake the advantages of developed areas should be made. For developed regions, investment in science and technology should be strengthened, talent training and introduction increased, and innovation capabilities improved. Finally, the linkage effect of regional high-speed rails will be realized, and the development efficiency of low-carbon tourism will be further improved.

Fourth, affected by COVID-19 in 2020, tourism carbon emissions will show a downward trend. It can be seen that it is not necessary to achieve sustainable development of tourism through the application of high efficiency and high technology. To a certain extent, this reflects that the low-carbon choice of tourists’ travel has a high degree of impact on sustainable tourism. At the same time, as the reduction of tourism carbon emissions, the technological progress changes and total factor productivity changes of high-speed rail to low-carbon tourism in 2020 will decline significantly. Although the technical level has been improved, the improvement of the overall efficiency of low-carbon tourism is still not obvious. The Chinese government should implement the concept of sustainable scientific development, practice the transformation of low-carbon life, publicize the concept of low-carbon life, and cultivate people’s awareness of low-carbon travel.

5.2. Research Discussion

According to the previous analysis, it is found that the average comprehensive development efficiency of high-speed rails for low-carbon tourism in Zhengzhou urban agglomeration is low, and the comprehensive development efficiency of each city in Zhengzhou urban agglomeration is quite different. From the perspective of spatial distribution, high-speed rails show an increasing trend for developing low-carbon tourism in some underdeveloped regions, and a downward trend for developing low-carbon tourism in more developed regions. From the perspective of time, the impact of high-speed rails on the development efficiency of low-carbon tourism in Zhengzhou urban agglomeration has great potential in the future. It is worth noting that in 2020, due to the impact of the COVID-19 pandemic, tourism carbon emissions are on a downward trend. This reflects that the low-carbon choice of tourists’ travel has a high degree of impact on sustainable tourism.

Based on the existing research, this paper not only verifies the adaptability of the DEA method to the development of tourism but also partially verifies the previous research on low-carbon tourism. It mainly includes the overall low level of low-carbon tourism efficiency and the importance of the low-carbon effect of high-speed rail on low-carbon tourism. At the same time, this study found that COVID-19 had a greater impact on the development efficiency of low-carbon tourism in Zhengzhou urban agglomeration. COVID-19 has led to a downward trend in tourism carbon emissions. To a certain extent, this reflects that problems such as air pollution and energy consumption crisis cannot be solved only by high efficiency and high technology. Developing a low-carbon lifestyle is the most effective way to solve environmental problems. Most scholars have recognized the low-carbon and energy-saving effects of high-speed rail, and people who travel long distances advocate choosing high-speed rail as the main mode of transportation.

This paper innovatively studies the development efficiency of low-carbon tourism with high-speed rail as the starting point, which not only verifies the recognition of the low-carbon effect of high-speed rail by existing research but also allows the government and low-carbon tourism development departments to maximize the use of high-speed rail when formulating development plans. It can also provide a reference for other industries when evaluating efficiency. For example, relying on high-speed rail stations to speed up the construction of tourism distribution centers give full play to the maximum effect of high-speed rail on the development of low-carbon tourism and increases the flow of high-speed rail tourists. So as to realize the rapid development of low-carbon tourism and provide an optimized path for sustainable tourism development.

There are still shortcomings in this study. Due to the time limit of the statistical data, data from 2021 to the present cannot be obtained. Therefore, the research time in this paper is defined as 2010–2020, which has certain limitations due to the fact that it is a short time span. Since this research has certain practical value, we plan to further deepen and improve this research with the passage of time and the continuous addition of data.