Estimation of Carbon Emissions from Tourism Transport and Analysis of Its Influencing Factors in Dunhuang

Abstract

Traffic carbon emissions have a non-negligible impact on global climate change. Effective estimation and control of carbon emissions from tourism transport will contribute to the reduction in the amount of global carbon emissions. Based on the panel data of Dunhuang in western China from 2010 to 2019, the process analysis method was used to estimate the carbon emissions from tourism traffic of Dunhuang. By establishing the Kaya identity of tourism traffic carbon emissions, the LMDI decomposition method was used to reveal the contribution of different factors to the change in tourism traffic carbon emissions. The results showed that the impact of tourism traffic carbon emissions was diversified; we found three main factors of promoting carbon emissions, namely the number of tourists, tourism expenditure per capita, and energy consumption per unit of passenger turnover. However, the contribution of tourism activities to GDP, passenger turnover per unit of GDP, and energy structure largely inhibited the increase in carbon emissions.

1. Introduction

The danger of global climate change has been under careful consideration by the international community for the past several decades. The massive burning of fossil fuels, especially the extensive use of internal combustion engine, have increased environmental pollution, and scientists are trying to deal with the problems by a series of remedial measures.

Carbon dioxide is the most important issue regarding global climate change. As carbon emissions continue to increase, the greenhouse effects are being produced in nearly every part of the world [1]. Tourism development is one of the sources of carbon emissions. Tourism growth not only contributes to an economy but also leads to a growth in energy consumption [2]; an increase in tourism activities is accompanied by an increased demand on energy such as catering service, accommodation, and transportation, especially expansion of tourism will lead to an increase in the massive burning of fossil fuels, thus, there is an urgent need to reduce carbon emissions from tourism transport [3,4]. Tourism activities are one of the potential contributors of climate change, accounting for a large portion of global carbon emissions. According to the latest data of the World Tourism Organization (UNWTO), carbon emissions related to tourism activities account for about 5% of the total emissions, and carbon emissions from tourism activities are expected to increase by 130% by 2035 [5,6]. Strongly developing tourism meant that greenhouse gas emissions have also increased correspondingly [7,8]. Therefore, the low-carbon development of tourism is an important subject to be discussed urgently.

2. Literature Review

2.1. Relationship between Carbon Emission and Tourism Activities

The Scholars have studied the relationship between tourism activities and carbon emissions. According to the literature review of related studies in China and abroad, it was found that the existing literature mainly focuses on the characteristics of carbon emissions from the tourism activities. For instance, Gössling estimated that CO₂ emissions of the global tourism activities reached 1400 Mt in 2001, which accounted for 5.3% of overall level of emission in that year [9]. In New Zealand, tourism has become the sixth most important sector in terms of energy consumption after metal manufacturing, living consumption, transportation, mining, warehousing and quarrying industry [10]. In Penghu Islands of the Taiwan Straits, China, tourism activities display an annual average energy consumption of 0.74 PJ [11]. For 27 African countries, tourism is positive factors leading to increased carbon emissions according to panel data spanning 2000 through 2020 [12]. Yiidirim et al. [13] studied the effect of visitor numbers on CO₂ emissions for Mediterranean countries. Gunter et al. [14] presented a method to assess transportation-related carbon dioxide emissions of tourism in European city. Studies have explored evidence that the carbon emissions are generated as part of going abroad to travel and indicated the need to reduce the carbon footprint of the Austrian tourism sector [15].

China is currently undergoing an acceleration of industrialization and urbanization. For this reason, China has ranked second in the world in terms of energy consumption [16]. Developed countries are calling for higher China’s allocation of carbon emission allowance; in this context, China is facing significant difficulties in saving energy and reducing carbon emissions [17]. In 2020, China received a total of 2.879 billion tourists, resulting in a huge amount of carbon emissions. Thus, there is an urgent need to reduce carbon emissions; carbon dioxide emissions must peak and then achieve carbon neutrality [18]. Based on the tourism satellite account and input-output model, the carbon emissions of tourism coupling system has established for China’s international tourism [19]. Using the tourism’s CO₂ emission data of China, tourism’s carbon emissions are growing significantly [20]. A total of 30 provincial administrative regions in China were selected as the basic research units, the influencing factors of tourism transportation carbon emission was explored [21]. Based on the panel data of China, scholars have also quantified the carbon emissions related to the specific aspect of the tourism, such as transportation infrastructure [22], tourism carbon footprint [23], and industrial structure [24].

2.2. Relationship between Carbon Emission and Transportation

While tourism activities have a profound impact on economic growth, it is important to point out that all tourism activities depend on fuel emissions energy [25]. Carbon emissions of the tourism activities have been rising every year and are globally experiencing a continuous upturn [26]; moreover, transportation was a primary source of carbon emissions [27]. Energy consumption of tourism transport accounts for 72.08% of the total tourism-related energy consumption [28]. Carbon emissions reduction has become a topic of great interest to researchers since low-carbon tourism is expected to be the dominant orientation of tourism development. With respect to carbon emissions from tourism transport, there is few research results related to carbon emissions based on spatial scale semantics. The LMDI decomposition method has been commonly used to build the carbon emissions model for regional tourism transport [29,30]. Therefore, it is of great significance to study the coupling coordination of carbon emission and transportation in China.

2.3. Decomposition of Tourism Traffic Carbon Emission Drivers

On the decomposition of tourism traffic carbon emission driving factors, scholars have carried out a lot of research. Chen et al. [31] decomposed the carbon emissions in China during 2000–2019 based on the bottom-up approach, maintaining the mutual-promoted and mutual-coordinated relations among different decoupling strategies in the tourism industry should be an important issue to governmental authorities [32]. Ma et al. [33] measured the carbon emissions of tourism traffic in Beijing by Tapio model and Logarithm Mean Divisia Index (LMDI) approach and discussed the relationship between tourism traffic carbon emissions and the influencing factors. LMDI decomposition method is the most common method used for estimating carbon emissions from tourism transport [34]. It has been reported that LMDI attribution was used to determine the factors affecting greenhouse gas emissions in the Beijing-Tianjin-Hebei region of China, the results showed that energy consumption was a dominant factor resulting in reduced greenhouse gas emissions [35]. One investigation applied the LMDI decomposition method to study changes in carbon emissions in the electricity sector [36], the LDMI method has the advantages of no residual error and strong applicability [37]. They use the LMDI decomposition method to study carbon emissions, and most of them study carbon emissions as a whole.

This paper aimed to evaluate and analyze the carbon emissions from tourism transport of Dunhuang in western China; apply the Kaya identity to determine carbon emissions of tourism traffic for the new tourist destination; and use the LMDI decomposition method to reveal the contribution of different factors to the change in tourism traffic carbon emissions. The historical and culture city of Dunhuang was taken as examples to fully realize the sustainable development of tourism industry.

3. Overview of the Study Area, Research Methodology, and Data Sources

3.1. An Overview of the Study Area

Dunhuang (92°13′ E–95°30′ E, 39°53′ N–41°35′ N) is located at the intersection of Gansu, Qinghai, and Xinjiang in western China. Rail, road, and civil aviation are the three major transportation options of Dunhuang, which is the most popular tourist attractions in western China and is best known as the hometown of the Flying Apsaras and the birthplace of Dunhuang Art.

3.2. Methodology

The two main methods to estimate carbon emissions include input-output analysis and process analysis [38]. The input-output analysis requires energy consumption statistics or carbon dioxide emissions monitoring data. However, the China Energy Statistical Yearbook does not provide statistics on tourism-related energy consumption [39]. Moreover China does not have carbon dioxide emission monitoring systems for different regions. Process analysis offers an indirect estimation of energy consumption in each aspect of tourism. Herein, we selected process analysis to estimate carbon emissions from passenger transportation.

3.2.1. Carbon Emissions from Passenger Transportation

Carbon emissions from transportation were estimated using the method developed by UNWTO [40]. For the three transportation options, which included rail, road, and civil aviation, the calculation model is shown in Equation (1):

$$C_i=P_i\times T_i$$

(1)

where C_i represents carbon emissions of the i-th transportation option; and P_i is the carbon emissions coefficient of the i-th transportation option (g CO₂/pkm). According to Kuo et al. [41], the carbon emissions coefficient (g CO₂/pkm) of the three transportation options, rail, road, and civil aviation, was 27, 133, and 137, respectively. T_i is the passenger turnover of the i-th transportation option (pkm).

3.2.2. Carbon Emissions from Tourism Transport

Carbon emissions from tourism transport can be estimated using Equation (2) [42]:

$${C^{\prime }}_i=C_i\times {\alpha }_i$$

(2)

where ${C^{\prime }}_i$ indicates carbon emissions of the i-th transportation option; ${\alpha }_i$ is the percentage of the carbon emissions of the i-th transportation option to those from tourism transport. For rail, road, and civil aviation, ${\alpha }_i$ was 31.6%, 13.8%, and 64.7%, respectively [43].

3.2.3. LMDI Decomposition Method for Decomposing the Factors of Influencing Carbon Emissions

LMDI decomposition method offers the benefit of computational convenience, solid theoretical basis, and extensive application [36]. Yoichi Kaya proposed the Kaya equation based on LMDI decomposition method [44]. The Kaya equation allows the incorporation of the main factors influencing carbon emissions from tourism transport to the LMDI method. These factors include economic development, number of tourists, energy structure, and industrial structure. The Kaya equation form of the expression is shown in Equation (3):

$${\mathrm{CO}}_2={\displaystyle \sum _{j}P\times \frac{R}{P}\times \frac{\mathrm{GDP}}{R}\times \frac{T}{\mathrm{GDP}}\times \frac{E}{T}\times \frac{E_j}{E}\times \frac{{\mathrm{CO}}_2}{E_j}}$$

(3)

where P represents the number of passengers; R is the tourism revenue; GDP indicates gross domestic product; T is the passenger turnover; E corresponds to energy consumption from transportation; and E_j is the consumption of the j-th energy.

Carbon emissions in the baseline period (C⁰) and the period t (C^t) are calculated using Equation (4):

$$\Delta {\mathrm{CO}}_2=C^t-C^0=\Delta C_P+\Delta C_l+\Delta C_r+\Delta C_G+\Delta C_q+\Delta C_s$$

(4)

where ΔCO₂ represents the variation in CO₂ emissions from transportation since the baseline year to the year t (ten thousand t); ΔC_p is the number of tourists; ΔC_l is the tourism expenditure per capita; ΔC_r indicates the contribution of the tourism activities to GDP; ΔC_G represents the passenger turnover per unit of GDP; ΔC_q corresponds to the energy consumption per unit of passenger turnover; and ΔC_s is the energy structure.

Referring to the LMDI decomposition method adopted by Ang [45], in LMDI additive decomposition, the variables are given by Equations (5)–(10):

$$\Delta C_P={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}\times }\ln \left[\frac{P^t}{P^0}\right]$$

(5)

$$\Delta C_l={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}\times }\ln \left[\frac{l^t}{l^0}\right]$$

(6)

$$\Delta C_r={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}\times }\ln \left[\frac{r^t}{r^0}\right]$$

(7)

$$\Delta C_G={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}}\times \ln \left[\frac{G^t}{G^0}\right]$$

(8)

$$\Delta C_q={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}\times }\ln \left[\frac{q^t}{q^0}\right]$$

(9)

$$\Delta C_s={\displaystyle \sum _{j=1}\frac{C_j^t-C_j^0}{\ln C_j^t-\ln C_j^0}\times }\ln \left[\frac{s^t}{s^0}\right]$$

(10)

where P corresponds to the number of tourists in Dunhuang, l = R/P, r = GDP/R, G = T/GDP, q = E/T, and s = E_j/E, 0 is the baseline period, t is a set period of time. When the results of Equations (5)–(10) were above 0, the corresponding variable has facilitated CO₂ emissions. If the results were below 0, the corresponding variable had an inhibitory effect on CO₂ emissions [46].

3.3. Data Sources

The study data was obtained from the Dunhuang Statistical Yearbooks (2010–2019) [47]. The panel data on passenger turnover of each transportation option came from the China Transportation Statistical Yearbook (2010–2019) [48] (see

Table 1. Tourist turnover in Dunhuang/10 thousand people.km.

Year	Tourist Turnover			Total
	Railway Travel	Road Travel	Air Travel
2010	108,360	40,320	40,840	189,520
2011	149,284	59,850	49,760	258,894
2012	220,500	96,790	63,000	380,290
2013	283,500	140,910	69,740	494,150
2014	330,024	199,140	65,140	594,304
2015	401,976	295,200	78,420	775,596
2016	395,460	422,320	99,300	917,080
2017	347,880	558,280	105,360	1,011,520
2018	321,084	733,960	151,540	1,206,584
2019	351,696	954,050	180,400	1,486,146

).

4. Results

4.1. Carbon Emissions from Tourism Transport and Dynamic Evolution

Carbon emissions from tourism transport in Dunhuang from 2010 to 2019 were calculated using Equations (1) and (2). Results are shown in

Table 2. Carbon emissions from tourism transport in Dunhuang from 2010 to 2019.

Year	Carbon Emissions from Tourism Transport/Mt			Total/Mt	Carbon Emissions per Capita/(kg/per Tourist)
	Rail	Road Travel	Air Travel
2010	0.00102	0.00082	0.00401	0.00585	3.4988
2011	0.00116	0.00100	0.00403	0.00620	3.2433
2012	0.00153	0.00145	0.00455	0.00754	2.9618
2013	0.00177	0.00190	0.00453	0.00820	2.7150
2014	0.00196	0.00255	0.00403	0.00854	2.4164
2015	0.00190	0.00299	0.00384	0.00873	2.3602
2016	0.00163	0.00375	0.00426	0.00965	2.4862
2017	0.00019	0.00673	0.00613	0.01305	3.1069
2018	0.00115	0.00566	0.00564	0.01245	2.7516
2019	0.00111	0.00649	0.00593	0.01354	2.7295

. The annual average growth rate of CO₂ emissions was 23.96%. In addition, the average annual growth rate of carbon emissions from rail, road, and air transports were 14.03%, 42.13%, and 17.95%, respectively. The annual average growth rate from tourism transport in Dunhuang was higher than the national average [37]. This occurred because Dunhuang is located in western China and the tourists usually travel long distances to get there. Moreover, western China has become one of the top national tourist destinations. According to the data in Table 1 and Table 2, variations in carbon emissions from tourism transport were similar to those of passenger turnover. In addition, the highest carbon emissions were those related to road travel, followed by air travel, and rail travel. As car ownership rate increases, the number of people choosing self-drive trips also increases, resulting in the continuous growth of carbon emissions from road travel. However, this finding disagreed with the suggestion that carbon emissions from air travel were the greatest [38]. The major reason for this discrepancy is that most tourists arrive in Dunhuang via rail travel and road travel. Therefore, in Dunhuang, carbon emissions attributed to air travel were not significantly high. As shown in Table 2, carbon emissions from air travel were significantly higher than those from rail travel. This indicated that the energy consumption in air travels was substantial. In addition, carbon emissions from air travel accounted for an increasing percentage of total carbon emissions from tourism transport. From 2010 to 2015, the rail passenger turnover was far higher than the passenger turnover of road travel and air travel. However, carbon emissions from rail travel were the smallest. Thus, rail travel is a low-carbon and environmental-friendly transportation option compared with road travel and air travel. Results in Table 2 indicated that per capita carbon emissions were 3.499 kg in Dunhuang in 2010. This level decreased to a minimum of 2.729 kg in 2019. The growth rate of tourists arriving in Dunhuang was generally higher than that of carbon emissions, which explained the decreasing trend of per capita carbon emissions.

4.2. Evolution Mechanism of Carbon Emissions from Tourism Transport

In the present study, CO₂ emissions from tourism transport were calculated by considering energy consumption. Tourism transport belongs to the transportation sector, thus, the LMDI decomposition method is also applicable. In the present research, the LMDI decomposition method was used to explore the factors that affect tourism transport carbon emissions. The carbon emissions coefficients (t of carbon/t of standard coal) of coal, petroleum, natural gas, and electricity that were used to estimate tourism transport carbon emissions in Dunhuang displayed values of 0.7329, 0.5574, 0.4226, and 2.2132, respectively [37].

The LMDI decomposition method and ”anel’data from the Dunhuang Statistical Yearbooks (

Table 3. Factors influencing tourism transport carbon emissions in Dunhuang from 2010 to 2019.

Year	CO2/Ten Thousand Tons	P/Ten Thousand Passengers	R/100 Million Yuan	GDP/100 Million Yuan	T/Ten Thousand Passengers·km	E/Ten Thousand Tons
2010	4.10	151.04	14.05	49.97	209,920	4.03
2011	4.56	180.00	18.00	63.60	236,594	5.13
2012	4.90	299.50	27.87	75.30	310,190	6.08
2013	5.32	410.03	38.30	87.80	362,150	7.13
2014	5.62	506.73	48.05	100.70	414,304	7.99
2015	5.97	669.39	62.76	102.17	428,596	8.43
2016	6.08	801.52	78.36	106.40	444,080	8.79
2017	6.11	639.95	91.33	110.93	458,428	8.85
2018	6.43	1077.3	115.00	120.78	506,584	9.75
2019	6.91	1337.33	149.69	132.72	551,146	10.82

) were used. Factors influencing tourism transport carbon emissions in Dunhuang were estimated using Equations (4)–(10). Results are presented in

Table 4. Decomposition of factors influencing carbon emissions of tourism transport in Dunhuang from 2010 to 2019.

Year	ΔCp	ΔCl	ΔCr	ΔCG	ΔCq	ΔCs	ΔCO2
2010	0.000	0.000	0.000	0.000	0.000	0.000	0.00
2011	0.34	0.14	−0.01	−0.24	0.24	−0.01	0.46
2012	1.46	0.00	−0.59	−0.04	0.04	−0.08	0.80
2013	2.34	0.01	−1.03	−0.04	0.06	−0.12	1.22
2014	2.99	0.05	−1.31	−0.05	0.01	−0.17	1.52
2015	3.80	0.02	−1.99	0.00	0.06	−0.01	1.87
2016	4.27	0.13	−2.47	−0.02	0.08	−0.02	1.98
2017	3.71	1.10	−2.76	−0.04	0.01	−0.01	2.01
2018	5.29	0.37	−3.28	0.00	0.01	−0.05	2.33
2019	6.23	0.53	−3.97	−0.03	0.06	−0.01	2.81

.

In the present research, we determined the degree of influence of different factors on carbon emissions from tourism transport. These factors included: (a) the number of tourists; (b) per capita tourism expenditure; (c) contribution of tourism activities to GDP; (d) passenger turnover per GDP unit; (e) energy consumption per unit of passenger turnover; and (f) energy structure. Our results are shown in

Table 5. Contribution rate of factors influencing tourism transport carbon emissions in Dunhuang from 2011 to 2019/%.

Year	Cp	Cl	Cr	CG	Cq	Cs
2011	74.66	30.79	−2.79	−51.75	51.73	−2.64
2012	183.77	0.10	−73.79	−5.26	5.58	−10.40
2013	191.96	0.80	−84.42	−3.52	4.85	−9.67
2014	196.68	3.12	−85.94	−3.39	0.74	−11.21
2015	203.31	1.08	−106.72	−0.19	3.31	−0.79
2016	215.86	6.43	−124.54	−0.84	3.95	−0.86
2017	184.89	54.81	−137.58	−2.10	0.71	−0.73
2018	227.30	15.92	−141.12	−0.18	0.29	−2.21
2019	221.43	18.79	−141.04	−1.17	2.27	−0.28

and Figure 1.

Tourism transport carbon emissions increased from 2010 to 2019, according to the results. It was also shown that number of tourists, energy consumption per unit of passenger turnover, and tourism expenditure per capita positively affected carbon emissions. In contrast, passenger turnover per GDP unit, contribution of the tourism activities to GDP, and energy structure displayed a negative effect on CO₂ emissions. As shown in Figure 1, the largest contribution to CO₂ emissions from tourism transport corresponded to the number of tourists; the contribution rate of this variable was the highest, reaching a 221.43% in 2019. The descending order of the absolute value of the contribution rate for other variables were: tourism activities to GDP (−114.01%); tourism expenditure per capita (18.79%); energy consumption per unit of passenger turnover (2.27%); passenger turnover per unit of GDP (−1.17%); and energy structure (−0.28%) in 2019. According to our results, we formulated the following conclusions:

(1)

The most important reason for the rising amount of CO₂ emissions from tourism transport was the increasing number of tourists. The number of tourists positively affected carbon emissions from tourism transport. Its contribution rate was also much more than that of other factors. The number of tourists arriving in Dunhuang has been growing rapidly in the past ten years. Since 2015, the annual average rate of the number of tourists has approached 25%. These facts have demonstrated that Dunhuang is attracting many tourists;

(2)

The increase in tourism expenditure per capita promoted the growth of carbon emissions from tourism transport, though to a lesser extent. With the improvement of people’s living conditions, tourism has become a popular leisure activity for an increasing number of people;

(3)

Dunhuang is located in western China, a far-off destination for many tourists. Therefore, the carbon emissions from tourism transport are higher in Dunhuang than in other China’s 5A scenic areas;

(4)

The development of tourism has a positive impact on GDP growth. Tourism is a low-polluting and highly profitable industry; our results showed that the constant development of the tourism activities resulted in carbon emissions reduction;

(5)

Passenger turnover per unit of GDP has a negative effect on carbon emissions. When other conditions are maintained constant, the larger the passenger turnover will lead to the lower the amount of carbon emissions. This result can be explained by China’s extensive construction of highways and high-speed railways, which have created favorable travel conditions;

(6)

Energy consumption for passenger transportation was the primary cause of increased carbon emissions from tourism transport. The statistics showed that the contribution rate of the passenger turnover per unit of GDP to carbon emissions from tourism transport had been decreasing over the years. This indicated that technical renewal and transformation can improve energy utilization efficiency, thereby reducing energy consumption and carbon emissions;

(7)

Energy structure had a negative impact on the growth of CO₂ emissions. The variation trend indicated that petroleum consumption has been decreasing, while the consumption of clean energies has been increasing. Clean energies such as natural gas, wind-generated electricity, and solar-generated electricity, will further reduce carbon emissions when used in the transportation sector;

(8)

The annual average growth rate of carbon emissions from tourism transport in Dunhuang was higher than in the rest of the region [14,33,37], the reason may be that Dunhuang is located in western China and the tourists usually travel long distances to get there. Moreover, western region of China has become one of the top national tourist destinations. The carbon emission was rising steadily in the areas of western region of China.

In summary, the number of tourists, tourism expenditure per capita, and energy consumption per unit of passenger turnover were the primary driving factors for the growth of carbon emissions from tourism transport in Dunhuang. In contrast, the contribution of the tourism activities to GDP, passenger turnover per unit of GDP, and energy structure were the main factors inhibiting carbon emissions. In other words, the number of tourists exerted the largest impact on carbon emissions from tourism transport. GDP was second in terms of the influencing degree, though it had a negative impact on CO₂ emissions from tourism transport. The third place corresponded to energy intensity. According to the statistics, even when energy intensity promoted carbon emissions from tourism transport, its influence declined in the studied decade. Energy structure represented the smallest influence, Herein, energy structure optimization reduced carbon emissions from tourism transport.

5. Conclusions and Discussion

In this study, the process analysis method was used to estimate the tourism traffic carbon emissions in Dunhuang from 2010 to 2019. The research results are the theoretical significance and important practical value for the low-carbon development of the tourism and provide a reference for other regions to control of carbon emissions from tourism transport.

According to the calculation results, the tourism transport carbon emissions have increased for the past 10 years. The air tour industry has been the most carbon emissions before 2016, however, road travel accounted for the largest proportion of carbon emissions after 2017. In addition, with the increase in the Dunhuang Mogao International Airport line, carbon emissions from air travel will increase in Dunhuang. At the same time, we concluded that tourism activities to GDP, passenger turnover per unit of GDP and energy structure were largely inhibited the increase in carbon emissions. That is to say that there is bidirectional causality between national income (GDP) and carbon emissions, energy use and economic growth.

6. The Main Policy Recommendation

According to the calculation and analysis results of carbon emissions from tourism transportation in Dunhuang, the practical policy recommendations for tourists to reduce carbon emissions are put forward as follows:

(1)

Dunhuang, as a world-renowned scenic spot and the nation’s key cultural relic protection site, has grown significantly in the past few years. As the numbers of tourists continue to grow, tourists should be selected with the low-carbon way to travel, such as electric vehicles or high-speed railway;

(2)

There is a need to promote low-carbon tourism and construct low-carbon tourist facilities in tourist destination, meanwhile, tourism management department and related enterprises in Dunhuang should actively participate in the advocacy of low-carbon tourism;

(3)

The relevant administrative department for tourism in Dunhuang should learn from the successful experience of other regions, such as defining appropriate standard and promoting the overall pattern of low-carbon tourism.

7. Research Prospect

The emphasis of this dissertation is Dunhuang’s carbon emissions from tourism transport and its influencing factors. main limitations of This study are as follows. First, the study was restricted to Dunhuang in western China; the direction of the future work is needed to explore other China’s 5A scenic areas in order to examine the differences of the influencing factors on the growths of carbon emissions in different types of tourism destinations. Second, the time span of 10 years is relatively short to investigate the long-term effect of carbon emissions from tourism transport.