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Journal of Resources and Ecology >

2021 , Vol. 12 >Issue 1: 56 - 67

DOI: https://doi.org/10.5814/j.issn.1674-764x.2021.01.006

Resource Economy

Temporal and Spatial Characteristics and Evolution of China’s Inbound Tourism Carbon Footprint

  • HAN Zhiyong , 1 ,
  • LI Tao , 2, 3, * ,
  • LIU Ximei 1
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  • 1. Ocean College of Agriculture University of Hebei, Qinhuangdao, Hebei 066003, China
  • 2. School of Geographic Science, Nanjing Normal University, Nanjing 210023, China
  • 3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*LI Tao, E-mail:

HAN Zhiyong, E-mail:

Received date: 2020-06-16

  Accepted date: 2020-09-01

  Online published: 2021-03-30

Supported by

National Natural Science Foundation of China(42001155)

National Natural Science Foundation of China(41571139)

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Abstract

Reducing carbon emissions and transitioning to a low-carbon economy are important propositions for human sustainability. Since it is closely related with high carbon emissions, international travel makes a substantial contribution to the global carbon emissions. To comprehensively explore the influence of international travel on carbon emissions and develop a sustainable development plan, this paper studies the temporal and spatial distribution and evolution of the carbon footprint of inbound tourism in China’s 30 provinces between 2007 and 2017. In this study, comprehensive calculations and spatial models are adopted to reveal the temporal and spatial characteristics. The results show that the carbon footprint of inbound tourism in China has been increasing continuously from 2007 to 2017. While the carbon footprint increased by 1.94-fold, from 5.623 million tons to 10.8809 million tons, it presented obvious fluctuations by initially increasing rapidly and then dropping slightly. From the perspective of the contributions of various tourism components on the carbon footprint, transportation and post and telecommunications account for the largest proportions. In the past ten years, the variations in the carbon footprint of inbound tourism in most provinces and cities in China were not very extreme, but maintained a relatively stable state. In the spatial dimension, the carbon footprint of China’s inbound tourism tends to decrease from the southeast to the northwest. The highest coefficient of variation is in Ningxia and the lowest is in Liaoning. Based on these results, recommendations are put forward for sustainable development plans in some major cities and provinces for the future.

Key words： carbon footprint; inbound tourism; spatial and temporal distribution; China

Cite this article

HAN Zhiyong , LI Tao , LIU Ximei . Temporal and Spatial Characteristics and Evolution of China’s Inbound Tourism Carbon Footprint[J]. Journal of Resources and Ecology, 2021 , 12(1) : 56 -67 . DOI: 10.5814/j.issn.1674-764x.2021.01.006

1 Introduction

Global climate change is an important phenomenon related to the sustainability of mankind, while carbon emission is a vital contributing factor. Tourism is currently one of the largest industries in the world, currently growing at an annual rate of 4%, and it has become one of the key factors among all human activities in exacerbating carbon emissions (UNWTO, 2015; Lenzen et al., 2018). Tourism accounts for 4.9% of global carbon emissions, and continues to increase at an annual average rate of 2.5% (Whittlesea et al., 2012; Huang et al., 2015). In particular, international travel is more closely related with the global carbon emissions, with its frequent use of transportation, hotels and electric power increasing CO2 emissions. Statistics show that China boasts one of the largest domestic tourism markets, also the third-ranked international tourist destination country in the world, and its total tourism carbon emissions ranks second, just following behind the United States (Lenzen et al, 2018; Zha et al., 2020). Thus, international tourism plays a critically important role in China’s carbon emissions reduction goals, which aim to reduce CO2 emissions per unit of GDP by 40%-45% based on 2005 levels (Li et al., 2012).
In 2018, inbound tourists in China numbered 141 million and generated 127.1 billion US dollars in income, representing increases of 1.2% and 3.0% over 2017 levels, respectively (China National Tourism Administration, 2019). On the one hand, inbound tourism has promoted local economic development; but on the other hand, it exacerbates the global carbon emissions. Therefore, the scientific analysis of carbon emissions of inbound tourists is of great significance, especially for promoting the energy savings, emission reductions and sustainable development of tourism and related industries.

2 Literature review

In the existing literature, studies on the tourism carbon footprint can be traced back to the 1990s. However, the connotation and denotation of the carbon footprint have not yet been determined fully. In terms of the connotation, some scholars believe that the carbon footprint should be a component of the ecological footprint, which is a unit area of land in the ecological sense (Browne et al., 2009; Ewing et al., 2010); while others believe the carbon footprint should be part of the life cycle assessment theory, making it a quality unit in the physics sense (Filimonau et al., 2011). As for the denotation, academic disagreements mainly focus on the specific composition of carbon emissions. When calculating carbon footprint, some scholars mainly take CO2 into consideration (Peters, 2010), while others would include other forms beside CO2, since they can also affect climate change (Ozturk et al., 2016). This paper supports the view of most scholars that the carbon footprint is part of the ecological footprint and only considers the emissions of CO2.
Studies on the tourism carbon footprint in the existing literature mainly focus on calculating the carbon footprint. Some research is conducted from the perspective of tourists (Juvan and Dolnicar, 2014; Sun and Pratt, 2014), while some is based on tourist destinations (Becken and Shuker, 2019; Rico et al., 2019; Sun, 2019). The latter focus is gradually becoming an important field in academic study. One example is Tang’s recent research on temporal and spatial evolution of the tourism carbon footprint and carbon efficiency from the perspective of tourist destinations (Tang et al., 2018; Tang et al., 2019). In addition, scholars have studied the tourism carbon footprint at various scales, such as scenic spots (Cheng et al., 2013), cities (Thongdejsri and Nitivattananon, 2019), provincial areas (Ren et al., 2019), countries (Sun et al., 2019) or the global scale (Sun, 2019; Haider and Akran, 2019). The calculation of the overall carbon footprint is converted from a calculation of the direct tourism carbon footprint into the direct plus indirect carbon footprints (Cadarso et al., 2016; Meng et al., 2016). This line of research has also been broadening, forming a series of research systems of different scales in scenic spot, cities, provinces, and countries. At the micro level, scholars have studied the internal structure of the tourism carbon footprint, generally concentrating on transportation (Wang et al., 2016; Peeters et al., 2019; Roukounakis et al., 2020), accommodation (De Grosbois and Fennell, 2011; Hu et al., 2015; Huang et al., 2015), entertainment and catering (Dwyer et al., 2010), etc. Most of the study results show that the carbon emission in transportation is the largest part of the tourism carbon footprint, accounting for 51%-91% (Liu et al., 2011), followed by accommodation and catering. The above review shows that studies based on carbon footprint research from the perspective of inbound tourists have made a great contribution to our understanding of the tourism carbon footprint.
However, in recent years, carbon footprint research from the perspective of different types of tourists has gradually attracted the attention of some scholars. For example, Wicker (2019) studied the carbon footprint of sports tourists, while Ciers et al. (2019) studied the air travel carbon footprint and Tang et al. (2015; 2019) studied carbon emissions from the perspective of heritage tourists. The research on the tourism carbon footprint has also become more detailed and in-depth, including studies on the carbon footprint of inbound tourists, whose carbon emissions are greater than those of other tourists due to their long journey distances and stay times. Sharp et al. (2016) has assessed the carbon footprint of Iceland’s inbound tourists based on life cycle analysis (LCA) with the contribution of a new method for calculating direct and indirect carbon footprints. Qureshi et al. (2019) studied the carbon footprints of inbound and outbound tourists in 35 countries from the perspective of the ecological footprint. As the third-ranked international tourist destination, carbon footprint research on inbound tourism in China will be highly representative of the international community and will serve as an important reference for international energy conservation, emission reduction and low-carbon tourism. The carbon footprint of domestic tourists in China has attracted many scholars’ attention (Fan et al., 2019; Ren et al., 2019; Xiong and Li, 2019; Xu et al., 2019), while research on the carbon footprint of inbound tourism still needs more study.
At the same time, the large number of inbound tourists to China presents obvious differences in their spatial and temporal characteristics. Therefore, calculating the carbon footprint of inbound tourists should place more emphasis on its temporal and spatial evolution, in order to better understand the characteristics of the inbound tourists carbon footprint in China, which will help the government to set its macro- control policy for the carbon footprint based on the regional differences. Hence, this paper studied the temporal and spatial evolution of the carbon footprint of inbound tourists in China, aiming to provide an in-depth understanding of the temporal and spatial distribution and evolution mechanism of the inbound tourism carbon footprint. The remainder of this paper is organized as follows: section 3 proposes the research methodology for inbound tourism carbon footprint determination based on comprehensive consumption and spatial analysis, while section 4 focuses on the results of the measurement, and section 5 presents the conclusions and a discussion.

3 Research data and methods

3.1 Research area and data source

The study area of this paper is limited to 30 provinces in the Chinese Mainland, excluding Tibet. Considering the availability of inbound tourism data, the research period is set from 2007 to 2017. The basic data used in this study, such as the income from inbound tourism, the number of regional inbound tourists, energy consumption, etc., are mainly from China Tourism Statistics Yearbook (2008-2018) and China Inbound Tourism Sample Survey Data (2008-2018), while the coefficients of carbon emission sources are from the IPCC Greenhouse Gas Emission Inventory Guide (2006).
Considering the large disparities between research areas, the efficiency of energy consumption of tourists in each province is different, and differences in energy consumption intensity and structure will seriously affect the carbon emissions (Li et al., 2013). To ensure the accuracy of the calculations, the carbon emission coefficient is introduced in this paper (Table 1), based on a previous study (Liu et al., 2019).
Table 1 Carbon emission coefficients of comprehensive energy consumption for the provinces of China (g MJ-1)
Province Average carbon
emission coefficient		 Province Average carbon
emission coefficient
Guizhou 23.82 Hubei 22.06
Gansu 23.64 Tianjin 21.78
Liaoning 23.50 Fujian 21.68
Shanxi 23.50 Shandong 21.61
Inner Mongolia 23.39 Hainan 20.82
Hebei 23.16 Jiangsu 20.80
Henan 23.10 Beijing 20.72
Hunan 23.04 Guangxi 20.65
Xinjiang 23.03 Chongqing 20.61
Yunnan 22.85 Zhejiang 20.59
Anhui 22.41 Shaanxi 20.54
Jilin 22.37 Guangdong 20.45
Heilongjiang 22.21 Sichuan 20.32
Ningxia 22.18 Qinghai 20.25
Jiangxi 22.08 Shanghai 20.13

Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.

3.2 Research models and methods

3.2.1 Basic definitions
Tourism is a comprehensive industry. Due to the lack of some key tourism-related statistics, it is almost impossible to calculate all of the various carbon footprints generated during the travel, especially the carbon footprint of international tourism. Therefore, in order to ensure the precision of this research and calculations, this paper clarifies several definitions.
(1) Traceability of carbon footprint consumption. The paper defines the tourists’ final consumption as their carbon footprint to overcome the calculation bias, based on the rule that the people who consume energy should be responsible for the carbon emission.
(2) Boundaries of carbon emissions. The tourism carbon footprint involves a process of spatial transfer from the tourists’ sources to their destinations. For the convenience of the calculation, according to the method adopted by most scholars, carbon emissions directly or indirectly generated by tourists should be included in those of the tourism consumption areas.
(3) Scope of the study. Travel involves multiple industries such as transportation, catering, post & telecommunications, and retail. Therefore, due to the difficulty in obtaining data, and based on the availability of relevant data and research, this paper will measure the carbon footprint of inbound tourism for the following three sectors: transportation and post-telecommunications (TPTS); accommodation, retail and catering (ARCS); and entertainment and consumption for living (ECLS) (Pan and Liang, 2016).
3.2.2 Comprehensive measurement model of the inbound tourism carbon footprint
The current popular methods for determining the tourism carbon footprint are the top-down input-output approach (IOA) and the bottom-up life cycle approach (LCA). The top-down method first requires statistics on the total energy consumption in tourism consumption, and it then calculates the carbon footprint based on the relevant energy consumption emission coefficient, as shown in a study by Liu et al. (2011) on Chengdu, and in a study by Sun (2014) on Taiwan Province. The adoption of this method has benefited from the gradual improvement in the statistical data on tourism energy consumption. Meanwhile, the bottom-up approach is the most commonly used method for calculating carbon footprints to account for energy consumption and emissions step by step, as in Cadarso et al. (2016). Some scholars have used both up-bottom and bottom-up methods to calculate the carbon footprint, and found contradictory results when they compared with resulting numeric values (Wiedmann and Thomas, 2009; Perch-Nielsen et al., 2010). However, Becken and Patterson (2006) calculated New Zealand’s tourism carbon footprint and found only a slight difference. Therefore, the two methods should be selected according to the comprehensive settings of the research areas. Generally speaking, it is more appropriate to adopt a bottom-up approach for a wider research scale with more complete tourism data, and a top-down method for a smaller research scale with less complete tourism data.
Considering that China’s tourism-related data are somewhat flawed and the particular characteristics of inbound tourism that are examined in this paper, a comprehensive estimation model and regional estimation model are adopted based on the top-down method. The calculation result includes the direct and indirect carbon footprint contributions of inbound tourism. At the same time, in order to ensure accuracy, this paper has introduced the stripping coefficient of inbound tourism consumption and the correction factor of the carbon emission intensity for the various provinces and cities of China (Pan and Liang, 2016; Liu et al., 2019).
(1) Comprehensive Estimation Model of the Inbound Tourism Carbon Footprint
According to the results of Liu et al. (2010), the formula to calculate tourism consuming energy i is as follows:
Ei = ei × si
In this formula, Ei represents the i energy consumed by the tourism industry; ei is the direct energy intensity for the i energy (tons of standard coal per 10000 yuan); and si refers to the aggregate income for the regions where the i energy was consumed in the tourism industry. Combined with the Kaya formula put forward by Japanese scholar Yoichi Kaya, the comprehensive calculation model of inbound tourism carbon footprint is as follows：
$ TCF=\sum{V\times {{E}_{i}}\times K} $
In this formula, TCF represents the total tourism carbon footprint; V represents the conversion coefficient of different energy sources translated into the standard coal units; Ei represents the amount of energy consumed by industry i in tourism; and K represents the carbon dioxide emission coefficient per unit of standard coal. Based on the previous definition and the basic statistical data of China, the comprehensive calculation model of the carbon footprint of inbound tourism in this paper is obtained as follows:
$ TC{{F}_{ij}}=\sum{V\times {{E}_{i}}\times K\times {{R}_{ij}}} $
In formula (3), TCFij indicates the carbon footprint of inbound tourism in i industry in year j, and Rij indicates the stripping coefficient of inbound tourism consumption for the i industry in j year. The calculation of Rij in formula 3 is shown in formula (4), where Tij represents the value added in i industry of tourism in the j year; and Vij represents the value of i industry added in j year.
Rij = Tij /Vij
(2) Regional Estimation Model of the Inbound Tourism Carbon Footprint
Introducing the environmental impact model of Ehrlich and Holden, “I=PAT”, the formula for the carbon emission intensity of inbound tourism is as follows (Cao et al., 2014; Dong et al., 2018):
Gj = TCFj /Aj
In formula (5), Gj indicates the carbon emission intensity of inbound tourism in year j; TCFj represents the total carbon footprint of inbound tourism in year j; and Aj represents the total revenue of inbound tourism in year j. Thus, the carbon footprint measurement model of inbound tourism for the different areas i in China is obtained as follows:
$ PC{{F}_{ij}}=\sum{{{G}_{j}}\times {{P}_{ij}}\times {{X}_{i}}} $
In this formula, PCFij indicates the carbon footprint of inbound tourism in area i in year j; Gj represents the carbon emission intensity of inbound tourism in year j; Pij represents the per capita expenditure of inbound tourism in i area in year j; and Xi indicates the carbon emission intensity correction factor for inbound tourism in area i.
3.2.3 Coefficient of variation
The coefficient of variation, also known as a “discrete variable”, is a standard indicator used to measure the degree of variation in observed data. It is calculated as shown in formula (7). Cv represents the coefficient of variation, which is the ratio of the standard deviation to the mean: where “σ” represents the standard deviation, while “μ” represents the arithmetic mean. The smaller the value of Cv, the lower the mean level of the variable, the smaller the measured value of the dispersion degree, and vice versa.
This paper takes the carbon footprint of inbound tourism in each province as the measurement index. When the Cv of a province or city is small, it indicates that the carbon footprint of the inbound tourism for this province is less discrete and the average annual amount of change is small, and vice versa.
$ {{C}_{v}}=\frac{\sigma }{\mu } $
3.2.4 Moran index
The Moran’s I was proposed by Park Moran in 1950. This paper takes the inbound tourism carbon footprint as a measurement index to determine whether there is significant spatial correlation between the inbound tourism carbon footprints of the various provinces and cities in China. The calculation method is shown in formula (8), in which $\bar{T}\bar{E}$ indicates the average carbon footprint of inbound tourism in various provinces, TEi indicates the inbound tourism carbon footprint of region i, and Wi,j indicates the spatial weight matrix of region i and region j. In the spatial weight matrix, i and j represent the two provinces or municipal districts to be measured, and when region i and region j are adjacent to each other, Wi,j=1, otherwise Wi,j=0. Therefore, the final Moran index I values range from -1 to 1, and when 0 <I<1, it indicates that there is a positive spatial correlation between the inbound tourism carbon footprints of the two provinces. In contrast, when -1<I<0, it indicates there is a negative spatial correlation between the inbound tourism carbon footprints of the two provinces; and when I=0, it indicates that there is no spatial relationship between the inbound tourism carbon footprints of the two provinces.
$ I=\frac{\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{n}{{{W}_{i,j}}}}\left| T{{E}_{{{i}_{{}}}}}-\bar{T}\bar{E} \right|\left| T{{E}_{j}}-\bar{T}\bar{E} \right|}{\sum\limits_{i=1}^{n}{\sum\limits_{j=1}^{n}{{{W}_{i,j}}}}\sum\limits_{i=1}^{n}{{{\left| T{{E}_{i}}-\bar{T}\bar{E} \right|}^{2}}}}\text{, } $
where ${{W}_{i,j}}=\left[ \begin{matrix} {{W}_{\text{l},1}} & \cdots & {{W}_{1,n}} \\ \vdots & \ddots & \vdots \\ {{W}_{n,1}} & \cdots & {{W}_{n,n}} \\ \end{matrix} \right]$.

4 China’s inbound tourism carbon footprint measurement

4.1 Measurement results and analysis

According to the data for the research area, this paper includes the stripping coefficient of inbound tourism consumption of China’s tourism industries from 2007 to 2017 (Table 2).
Table 2 Stripping coefficient of inbound tourism consumption (unit: %)
Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
(TPTS)sc 6.85 6.30 5.80 5.63 5.26 5.23 4.69 4.80 10.11 10.36 9.39
(ARCS)sc 5.80 3.66 3.27 3.24 2.54 2.14 1.98 1.93 3.93 3.31 3.33
(ECLS)sc 1.47 1.14 0.96 0.92 0.81 0.70 0.63 0.60 1.01 1.14 1.02

Note: TPTS represents transportation and post-telecommunications; ARCS represents accommodation, retail and catering; ECLS represents entertainment and consumption for living; sc represents stripping coefficient.

Based on the data in Table 1 and Table 2, the carbon footprint of China’s inbound tourism from 2007 to 2017 is calculated as shown in Table 3. On the whole (i.e., in the row marked “total”), the carbon footprint of inbound tourism in China increased from 562.30 tons in 2007 to 1088.09 tons in 2017, an increase of 1.94-fold, which is less than the 2.45-fold increase in the domestic tourism carbon footprint calculated by Liu et al. (2019). In recent years, the growth tendency of inbound tourism in China has been lower than that of domestic tourism, and even lower than that of outbound tourism, so these results align with the expectation based on realistic logic. At the same time, the carbon footprint produced by transportation and post-telecommunication accounted for the largest proportion, which is consistent with the results calculated by scholars(Wang et al., 2016; Liu et al., 2019; Peeters et al., 2019; Roukounakis et al., 2020).
Table 3 Carbon footprint of inbound tourism in China from 2007 to 2017 (´104 t )
Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
(TPTS)cf 451.66 316.48 300.95 334.27 341.96 372.79 357.66 382.51 848.85 899.97 867.81
(ARCS)cf 41.52 45.97 45.89 55.77 50.88 46.89 46.07 45.93 98.32 87.14 91.01
(ECLS)cf 69.11 79.93 71.55 73.43 70.33 64.63 62.75 61.76 110.40 135.14 129.23
Total 562.30 442.37 418.38 463.48 463.18 484.31 466.48 490.20 1057.56 1122.25 1088.09

Note: TPTS represents transportation and post-telecommunications; ARCS represents accommodation, retail and catering; ECLS represents entertainment and consumption for living; cf represents carbon footprint.

Next, the carbon emission coefficients of each province were standardized, and the correction factors of carbon emission intensity for each province are obtained, as shown in Table 4.
Table 4 Correction factors of carbon emission intensity for the provinces and cities of China
Province Correction factor Province Correction factor
Beijing 0.95 Henan 1.05
Tianjin 0.99 Hubei 1.01
Hebei 1.06 Hunan 1.05
Shanxi 1.07 Guangdong 0.93
Inner Mongolia 1.07 Guangxi 0.94
Liaoning 1.07 Hainan 0.95
Jilin 1.02 Chongqing 0.94
Heilongjiang 1.01 Sichuan 0.93
Shanghai 0.92 Guizhou 1.09
Jiangsu 0.95 Yunnan 1.04
Zhejiang 0.94 Shaanxi 0.94
Anhui 1.02 Gansu 1.08
Fujian 0.99 Qinghai 0.92
Jiangxi 1.01 Ningxia 1.01
Shandong 0.99 Xinjiang 1.05

Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.

According to formulas (5) and (6), combined with Tables 3 and 4, the carbon footprints of inbound tourism for each province in China from 2007 to 2017 are calculated (see Table 5). These results show that the total carbon footprints of inbound tourism in Guangdong, Shanghai and Beijing have ranked first, second and third among the total carbon footprints of inbound tourism in China in the past decade, which is consistent with the results of Pan and Liang (2016). Guangdong, Shanghai and Beijing have always attracted more inbound tourists than that of any other cities in China, and their state of tourism development is highly mature, thus their carbon footprints of inbound tourism are always in the forefront. These top three are followed by Jiangsu, Zhejiang and Fujian, which also attract relatively large numbers of inbound tourists, due to their abundant tourism resources, and convenient and superior geographical locations. Meanwhile, Gansu, Qinghai and Ningxia, with disadvantages of their locations and transportation structures, have fewer inbound tourists and they rank among the last three in terms of inbound tourism carbon footprint among the provinces and cities in China.
Table 5 Calculation results of the carbon footprint of inbound tourism in China from 2007 to 2017 (´104 t)
Province 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Beijing 65.65 47.03 40.71 42.63 39.84 35.51 32.68 32.93 64.44 72.67 65.35
Tianjin 11.73 11.10 11.62 12.61 13.57 16.14 18.56 22.47 48.51 53.59 50.24
Hebei 4.95 3.23 3.22 3.31 3.68 4.20 4.46 4.27 7.85 8.85 8.24
Shanxi 3.60 3.60 4.01 4.45 4.73 5.63 6.36 2.28 4.72 5.16 5.06
Inner Mongolia 8.82 6.87 5.89 5.74 5.57 6.01 7.40 8.09 15.20 18.43 17.91
Liaoning 19.96 18.26 19.67 21.65 22.63 25.53 26.88 13.11 25.97 29.65 25.69
Jilin 2.78 2.41 2.45 2.78 3.06 3.68 4.06 4.50 10.94 12.24 10.53
Heilongjiang 9.87 9.84 6.40 6.91 7.23 6.18 4.42 4.32 5.93 7.04 6.55
Shanghai 65.08 50.95 43.07 52.06 41.10 36.81 34.73 38.89 79.67 89.39 82.91
Jiangsu 49.92 41.08 37.68 40.58 41.74 43.61 16.28 21.75 49.55 54.73 53.65
Zhejiang 38.57 31.70 29.94 33.00 33.20 35.31 36.52 40.86 94.40 44.55 45.40
Anhui 5.33 5.18 5.72 6.48 9.38 11.66 12.24 14.22 34.25 39.41 39.69
Fujian 32.54 26.41 25.42 26.33 27.97 30.49 32.61 36.72 81.43 99.37 101.15
Jiangxi 2.99 2.83 2.89 3.12 3.25 3.56 3.81 4.24 8.45 8.93 8.55
Shandong 20.21 15.30 17.21 18.99 19.57 21.03 19.41 17.37 42.27 45.79 42.17
Henan 5.08 4.40 4.51 4.70 4.50 4.70 5.01 4.29 9.73 10.33 9.40
Hubei 6.30 4.97 5.08 6.76 7.36 8.83 8.84 9.42 24.91 28.57 28.55
Hunan 10.24 7.24 6.99 8.51 8.30 7.12 6.23 6.36 13.35 16.01 18.35
Guangdong 123.16 95.51 92.50 103.27 100.95 106.26 109.50 120.65 247.00 262.80 250.98
Guangxi 8.24 6.32 5.99 6.79 7.71 8.79 10.51 11.20 26.73 30.92 30.42
Hainan 4.34 3.33 2.60 2.74 2.78 2.41 2.31 1.93 3.49 5.04 8.72
Chongqing 5.45 4.72 4.99 5.91 7.08 8.01 8.60 9.63 20.44 24.05 24.68
Sichuan 7.20 1.59 2.64 2.93 4.28 5.40 5.11 6.01 16.20 22.23 18.07
Guizhou 2.13 1.42 1.19 1.26 1.14 1.34 1.58 1.55 3.72 4.16 4.15
Yunnan 13.59 11.72 12.08 12.33 13.05 14.81 18.18 19.08 44.37 48.60 49.88
Shaanxi 8.70 6.90 7.14 8.51 9.44 10.92 11.32 12.53 27.75 33.23 34.15
Gansu 1.15 0.19 0.13 0.14 0.15 0.18 0.16 0.08 0.23 0.31 0.30
Qinghai 0.22 0.11 0.14 0.17 0.19 0.16 0.13 0.17 0.53 0.62 0.48
Ningxia 0.04 0.03 0.04 0.05 0.05 0.04 0.09 0.14 0.31 0.62 0.51
Xinjiang 2.58 1.59 1.42 1.74 3.80 4.22 4.43 3.95 8.65 8.26 11.48

Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.

4.2 Temporal evolutionary changes of the inbound tourism carbon footprint

Figure 1 is compiled according to Table 5 in order to better understand the temporal evolutionary changes in the carbon footprint of inbound tourism in China. The total carbon footprint of inbound tourism in China has been increasing continuously from 2007 to 2017. While 2008 and 2009 show the lowest carbon footprints of inbound tourism in China, this is consistent with the economic environment at that time. The outbreak of the global economic crisis led to a decrease in the number of tourists around the world. As a result, the total carbon footprint of inbound tourism in China was also at its lowest level in a decade. At the other end of the spectrum, 2015 saw the fastest growth of inbound tourism in China in this decade, with the carbon footprint increasing nearly 2.16-fold compared with 2014. This growth is related to the recovery of the global economy and the increase of inbound tourists in China. After that, the inbound tourism carbon footprint in China began to fall starting in 2017, as Chinese per capita energy consumption decreased.
Fig. 1 Total carbon footprint of China’s inbound tourism from 2007 to 2017
According to Table 5 and formula (7), the mean carbon footprints and coefficients of the provinces and cities in the past 10 years are calculated and rearranged into Table 6. From the perspective of the coefficient of variation, the highest coefficient of variation is 1.04 in Ningxia, which indicates that the carbon footprint of inbound tourism in Ningxia has fluctuated the most in recent years. The lowest level for Ningxia was 0.03 million tons in 2008 and the highest was 0.62 million tons in 2016, representing an increase of nearly 18-fold. In contrast, Liaoning has the lowest coefficient of variation at 0.18, indicating that the carbon footprint of inbound tourism in Liaoning fluctuates very little, having been maintained at around 24.90 million tons in the past ten years. Among all provinces in China, Ningxia, Gansu, Sichuan, Anhui and Hubei have higher coefficients of variation, while Liaoning, Shanxi and Heilongjiang have lower coefficients of variation. In recent years, the provinces with high coefficients of variation have developed well with respect to inbound tourism, especially Ningxia, where the total income of inbound tourism has increased nearly 12-fold. However, most of the provinces with high coefficients of variation are concentrated in the Midwest, which is also related to factors such as the long transportation distance of inbound tourists, and low infrastructure and energy efficiency. Most of the provinces with low coefficients of variation are concentrated in the eastern economically developed areas, which is related to the stability of the development of inbound tourism in that region.
Table 6 Mean and CV of carbon footprint of inbound tourism in China's provinces from 2007 to 2017
Province Mean of carbon footprint (×104 t) Coefficient of variation Province Mean of carbon footprint (×104 t) Coefficient of variation
Guangdong 161.26 0.41 Hunan 10.87 0.37
Shanghai 61.46 0.31 Inner Mongolia 10.59 0.45
Beijing 53.94 0.26 Sichuan 9.17 0.73
Fujian 52.04 0.56 Heilongjiang 7.47 0.23
Zhejiang 46.34 0.37 Henan 6.67 0.35
Jiangsu 45.06 0.26 Jilin 5.94 0.61
Shandong 27.93 0.40 Hebei 5.63 0.36
Tianjin 27.02 0.61 Jiangxi 5.26 0.46
Yunnan 25.77 0.58 Xinjiang 5.21 0.61
Liaoning 24.90 0.18 Shanxi 4.96 0.21
Anhui 18.36 0.72 Hainan 3.97 0.46
Shaanxi 17.06 0.60 Guizhou 2.36 0.50
Guangxi 15.36 0.62 Gansu 0.30 0.94
Hubei 13.96 0.65 Qinghai 0.29 0.60
Chongqing 12.36 0.60 Ningxia 0.19 1.04

Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.

4.3 The spatial evolution of carbon footprint for inbound tourism

In order to more clearly show the spatial evolution of the carbon footprint of inbound tourism in China from 2007 to 2017, the Moran index is calculated according to formula (8), and the software GeoDa1.12 is used for visualization. Scatter plots of the Moran index for 2007, 2010, 2014 and 2017 are shown in Fig. 2. The P value of the calculation result is less than 0.05 and it passed the Z test. There are four quadrants in each of the scatter plots. The first quadrant is the high-high aggregation area, indicating that each province in this quadrant has a higher inbound tourism carbon footprint, a smaller spatial difference and a stronger positive spatial correlation with its surrounding areas. The second quadrant is for low-high agglomeration areas, which indicates that the carbon footprints of inbound tourism for those provinces show strong heterogeneity, and have a strong negative spatial correlation. In this quadrant, the inbound tourism carbon footprint of a low-value area is surrounded by high-value areas. The third quadrant is the low-low agglomeration area, which indicates that the provinces in this area are blind spots for the carbon footprint of inbound tourism. The difference between the province itself and the surrounding areas is low, so it presents a strong positive correlation, with the low carbon footprint area of inbound tourism being surrounded by other low-value areas. The fourth quadrant is for high-low agglomeration areas, which also indicates that the carbon footprint of inbound tourism in each province and city in this area is heterogeneous. In this quadrant, the carbon footprint of inbound tourism is high, but the surrounding areas have very low carbon footprints, indicating the province with high carbon footprint of inbound tourism is surrounded by low-value areas.
Fig. 2 Scatter plots of Moran’s I (2007, 2010, 2014, 2017)
As shown in Fig. 2, the Moran index from 2007 to 2017 has an overall trend of slow growth. The larger the Moran index, the more obvious the spatial correlation will be, indicating that the spatial correlation of the carbon footprint of inbound tourism in China is becoming much stronger. This trend shows that the inbound tourists may choose more than one province as their destinations in China. Therefore, the tourism correlation between provinces is becoming higher. In the last ten years, the carbon footprint of inbound tourism in China has shown a distinct trend toward more provinces aggregating in the low-low and high-high concentration areas, with those in the low-low concentration area mainly located in the inland portions of central and western China, while the provinces in the high-high concentration area are mainly in the economically developed eastern regions. Inner Mongolia, Xinjiang, Gansu, and Shaanxi are always located in the low-low value concentration area. Owing to location and traffic factors, inbound tourists in those provinces have been relatively few, and the carbon footprint of inbound tourism also shows a low correlation. However, although it is in the low-low concentration area, Ningxia’s correlation with the surrounding areas is not strong. Fujian has always been in the high-high concentration area, which indicates that the carbon footprint of its inbound tourism is closely related to the surrounding areas. It is worth noting that Sichuan has begun to separate from the low-low concentration area since 2010, which has something to do with the development of tourism in the region. Sichuan is a popular international destination which is gradually attracting increasing numbers of tourists with its unique natural and cultural heritages, which is causing the carbon footprint of inbound tourism to rise. On the whole, the correlations of carbon footprints of inbound tourism between various provinces in China have remained relatively stationary.
In order to better understand the spatial evolution characteristics of the carbon footprint of inbound tourism in China in the past ten years, a quantile analysis of Table 4 was carried out by GeoDa1.12. As shown in Fig. 3, the carbon footprint of inbound tourism in the eastern coastal areas has changed little in the past decade, and Beijing, Guangdong, and Fujian have remained in the first echelon. This analysis shows that the carbon footprints of inbound tourism in these three provinces always stayed in the top three, and the numbers of inbound tourists are also among the top three in China. Heilongjiang, Liaoning, Shandong and Hebei in the east show downward trends in their carbon footprints of inbound tourism. Heilongjiang and Liaoning dropped from the second in 2007 to the fifth and fourth places, respectively, in 2017. It is worth noting that the carbon footprints of inbound tourism in Jiangsu and Tianjin in the east have increased rapidly, placing them in the first echelon in recent years, with obvious pressures of energy saving and emission reduction.
Fig. 3 Spatial distribution of the inbound tourism carbon footprint in 2007, 2010, 2014 and 2017.

Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.

In the central and western regions, Gansu, Qinghai and Ningxia have always been in the fifth echelon, which belongs to the provinces with lower carbon footprints of inbound tourism in China. The provinces with the greatest changes in their carbon footprints are Xinjiang, Inner Mongolia and Shanxi, among which Xinjiang shows an upward trend, while the latter two show downward trends. In recent years, the number of inbound tourists in Xinjiang has increased, and the carbon footprint of inbound tourism has also increased. Since this province is in the ecologically fragile west of China, more attention needs to be paid to its energy conservation and emission reduction. Generally speaking, the spatial differences of carbon footprints of inbound tourism among the provinces in China are obvious, showing a decreasing spatial distribution trend from southeast to northwest.

5 Discussion

Energy conservation and carbon emission reduction in the tourism industry have always been important topics in tourism academic research, such as the in-depth analyses of major domains in transportation (Wang et al., 2016; Peeters et al., 2019; Roukounakis et al., 2020), accommodation (De Grosbois and Fennell, 2011; Hu et al., 2015; Huang et al., 2015), and catering (Dwyer et al., 2010), which have realized great achievements. Despite the fact that China is the world’s largest inbound tourism market (China National Tourism Administration, 2019), scholars have provided little research on its inbound tourism carbon footprint. Most previous studies on the carbon footprint of tourism are only based on cross-sectional data from individual or regional tourism perspectives, with less focus on the carbon footprint of inbound tourism, or they have only taken the carbon footprint of inbound tourism as a part of the research content (Cao et al., 2014; Dong et al., 2018). From the policy perspective, China has introduced a series of energy-saving and emission-reduction policies to reduce the carbon emissions, such as the “Several Opinions on the Tourism Industry’s Response to Climate Change Issues” and the “Opinions of the State Council on Accelerating the Development of Tourism”. However, due to the great regional differences in the types of tourists and other factors, these policies lack a driving pertinence. Therefore, from the perspective of inbound tourism, this paper studies the spatial and temporal evolution of China’s inbound tourism carbon footprint over the past ten years, which has strong theoretical and practical significance. In terms of calculation methods, in order to ensure the accuracy of the calculations, tourism consumption stripping coefficients and carbon emission correction factors for each city and province of China are introduced. It is necessary to consider the carbon emission factors of the provinces in China for their residential energy consumption due to the relationship between carbon dioxide emission intensity and economic growth (Li et al., 2013). Through comprehensive measurement models, the carbon footprints of inbound tourism for the various provinces in China have been calculated for the past ten years, providing a reference for the provinces and cities to formulate corresponding measures for energy conservation and emission reduction.
In this research, we found that there are large differences in the carbon footprints of inbound tourism among China’s provinces and cities. Different provinces and cities have different responsibilities to undertake in managing the carbon footprint of their inbound tourism: provinces with higher carbon footprints should bear more responsibility for emission reduction, establish stronger institutional restraint mechanisms for tourism consumption, reduce energy intensity through low-carbon technologies, and optimize the energy structure; meanwhile, provinces with low carbon footprints should take advantage of national policy opportunities to vigorously develop inbound tourism, learn from the eastern part on their experiences in energy conservation and emission reduction, and introduce advanced technologies to cope with the dual pressures of development and energy conservation and emission reduction. In addition, we found that the transportation carbon footprint is the main component of the inbound tourism carbon footprint, which is consistent with the results of domestic tourism carbon footprint research (Wang et al., 2016; Peeters et al., 2019; Liu et al., 2019; Roukounakis et al., 2020). The spatial structures of tourist sources and the choices of tourist transportation methods affect the intensity of the tourism carbon footprint. Therefore, this is exactly what Chinese provinces and cities need to pay attention to when formulating energy conservation and emission reduction policies.
This paper studies the temporal and spatial evolution characteristics of China’s inbound tourism carbon footprint from a macro perspective. At the micro level, such as the efficiency of the inbound tourism carbon footprint in each province, and the carbon footprint contributions produced by transportation, accommodation and entertainment of inbound tourism, further research is needed. Therefore, it is hoped that this study can play a role in inspiring others to perform further research and make more valuable contributions. Furthermore, in order to study the carbon footprint of inbound tourism in-depth, it is necessary to strengthen and improve the overall research framework, basic data acquisition and research methods.

6 Conclusions

In this paper, the total carbon footprints of inbound tourism in 30 provinces (cities) of China from 2007 to 2017 are measured, and the temporal and spatial evolution of these footprints are studied by means of the coefficient of variation, Moran index and quartile. The following conclusions are drawn:
(1) The carbon footprint of inbound tourism in China has shown a rapid rise and then a slight fall. From 2007 to 2014, the overall situation was stable, with rapid growth in 2015 and a slight decline in 2017. Its rapid growth has a closer relation with the sustainable development of inbound tourism in China, and the slight decline shows that China has made initial advances in achieving energy conservation and emission reduction. In the past ten years, the total carbon footprint of inbound tourism in China has nearly tripled, accounting for 10% of the total carbon footprint of tourism on the whole (Wang et al., 2017). However, while promoting low-carbon tourism in various provinces and cities, policy makers tend to ignore the inbound tourists. Many policies in various provinces and cities have only considered local tourists, without subdividing different types of tourists, thus reducing the effectiveness of low-carbon tourism. From the results of both growth trend and total value, the carbon footprint of inbound tourism is an important reference in carbon emission management. The local provincial governments should formulate different energy conservation and emission reduction policies according to their own situations and form assessable plans to mediate inbound tourism and tourists, enhancing the pertinence of policies designed to reduce the carbon footprint.
(2) In the temporal dimension, the degree of variation among the carbon footprints of inbound tourism in different provinces are different. The research results show that the provinces with stronger tourism economies, such as Beijing, Shanghai and Guangdong, which have attracted large numbers of international tourists, benefit from their obvious location advantages and famous tourism resources, maintaining high carbon footprints of inbound tourism over the past ten years. Therefore, in response to the energy conservation and emission reduction of inbound tourism, their relevant policies should be consistent and play a representative role. However, it is worth noting that the carbon footprints of inbound tourism in Ningxia, Gansu, Sichuan, Hubei, Anhui and other provinces have changed greatly in the past decade. The pressure on energy conservation and emission reduction is relatively large. There should be flexibility in formulating policies for responding to the problems of the carbon footprint. In some provinces in central and western China, the carbon footprint of inbound tourism has been less volatile in the past decade, having a positive correlation with its tourism development level. With unpopular but unique resources, these provinces must find ways to boost inbound tourism, and maintain the stable and low level of their inbound tourism carbon footprints.
(3) In the spatial dimension, the carbon footprints of inbound tourism show a downward trend from the southeastern provinces to the northwestern provinces. In the past ten years, China’s inbound tourism carbon footprint spatial agglomeration mode did not change much. The western and northwestern provinces maintained low agglomeration, and the eastern coastal provinces maintained high agglomeration. Spatial distribution intensity in some central and western provinces changed, but these changes were not very large. Overall, the downward trend from the southeast to the northwest suggests that the provinces in different regions should consider different priorities in dealing with the carbon footprint of inbound tourism. The eastern region, with a more developed economy and obvious regional advantages, whose inbound tourism has a high carbon footprint, should take more responsibility for energy conservation and emission reduction, especially in Guangdong Province where the total carbon footprint of inbound tourism has always ranked first in the country. Guangdong should play a leading role in low-carbon tourism and low-carbon consumption while developing its economy. In the western regions, the economy is relatively underdeveloped, transportation is poor, and the total carbon footprint of inbound tourism is low. However, due to the fragility of their ecological environment, western regions face the dual pressures of developing inbound tourism while achieving energy conservation and emission reduction. With the further development of western China and the implementation of the Belt and Road policy, the central and western regions not only have new opportunities for tourism development, but they also face new conditions for energy conservation and emission reduction. Therefore, while seizing the development opportunities, the central and western regions should learn from the experiences of the eastern regions in energy conservation and emission reduction, to avoid the old road of polluting first and controlling the pollution afterwards.

The authors thank to The Innovation and Entrepreneurship Doctoral Fund of Jiangsu Province (to Li Tao).

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