1. Introduction
The tourism carbon footprint is an extension of the carbon footprint theory, whose core concept relates to the amount of greenhouse gases, including CO2, released to meet the energy needs of tourists. Due to the large mobility of tourists and the high level of energy consumption of tourism activities, carbon emissions have skyrocketed in the tourism industry, which was once considered a green industry. Scientists at the University of Sydney estimated that the total annual carbon emissions of the tourism industry worldwide in 2018 were close to 4.3 billion tons, accounting for 8% of all global carbon emissions [1], indicating that carbon emissions from tourism have a significant impact on climate change [2,3,4,5,6]. As a result, the study of tourism’s carbon impact has become a research hotspot, hence, this study.
There are two popular approaches for calculating the carbon footprint of tourism: the “bottom-up” method, which is based on life-cycle process analysis, and the “top-down” method, based on environmental inputs and outputs. In some studies, the carbon footprint of tourism in China (e.g., western China [7], Taiwan [8], Shandong Province [9], Jiangsu Province [10], Jiuzhaigou, Sichuan Province [11], and China [12]) and other countries (e.g., Spain [13] and Wales, UK [14]) was estimated using the “top-down” method, while the “bottom-up” method was used to examine the carbon footprint of tourism in China (e.g., Liaoning Coastal Economic Zone [15], Gansu Province [16], Jigongshan Scenic Area, Henan Province [17], Rizhao City, Shandong Province [18], Lijiang City, Yunnan Province [19], Yangtze River Delta [20], Shandong Province [21], and Jiujiang City, Jiangxi Province [22]) and other countries (e.g., Iceland [23], Thailand [24], New Zealand [25], and Sweden [26]. The results of the previous studies are not quite comparable. This may be attributable to the inconsistency of the connotation and extension of the concept of tourism carbon footprint, diverse statistical data source, and different boundaries of the measuring system as determined by various studies. Tourism carbon footprints comprises both direct and indirect parts. The “bottom-up” method based on the life-cycle assessment theory is a product-oriented carbon footprint analysis. Here, carbon footprints are measured based on the actual consumption of tourists, which may disregard the indirect carbon footprint of tourism. The “top-down” method based on environmental input-output theory need to firstly obtain the total energy consumption of tourism industry, and then calculate tourism carbon footprints using the carbon emission coefficient for each type of energy. Although both direct and indirect tourism carbon footprints can be calculated in this way, it is not easy to determine energy consumption statistics and carbon emission monitoring data (at the national or regional level) since many countries and regions lack these data. What’s more, the lack of a detailed consideration of the impact of multiple decisions of consumer choices (different modes of transport, different types of accommodation hotels, etc.) on tourism carbon footprints may lead to research findings less comprehensive. Therefore, it is more reasonable to establish the system boundary of tourism based on Tourism Satellite Account and then to account for the carbon footprint of tourism so that data and analyses are comparable.
A review of existing studies shows that there is room for improvement in studies on tourism carbon footprints. First, the studies on tourism carbon footprint cover various geographical scales, but most of them focus on the national (or regional) scale, and there are few studies on the carbon footprint of a small scale, especially the carbon footprint of a tourist scenic area, and this paper focuses on the study of the carbon footprint of a tourist scenic area. Second, previous studies on the carbon footprint of tourism have ignored the multiple decisions made by tourists themselves; this study uses the life-cycle method to develop a model suitable for “bottom-up” analysis based on the multiple decisions made by tourists (different transportation modes, different levels of hotels, different tourism activities, etc.).
The contribution of this paper is mainly twofold: firstly, based on the panel data of Chenzhou City, China, we measure the carbon footprint of tourism in Chenzhou City and conduct a time analysis to reflect the current situation and evolution trend of tourism development in Chenzhou City from a dynamic perspective; secondly, based on the multiple decisions of tourists’ choices, we adopt the “bottom-up” method to decompose the carbon footprint of tourism into seven components: transportation, catering, accommodation, sightseeing activities, shopping, entertainment and waste disposal, analyze and compare the differences in the total carbon footprint and composition of domestic and international tourists., and seek the main factors that have the greatest influence on the carbon footprint of tourism, so as to provide a scientific basis for formulating the right strategy of low-carbon tourism and maintaining the policy of sustainable development of tourism industry.
2. Materials and Methods
2.1. Study Area
Chenzhou City is located in the south of Hunan Province, China, between 24°53′ and 26°50′ North latitude and 112°13′ and 114°14′ East longitude, bordering Guangdong Province, which has the most developed tourism sector among coastal provinces in China. Studying the carbon footprint of tourists in Chenzhou City can provide insights for future research on inland mountainous regions in central China (Figure 1).
Chenzhou is situated at the juncture where the Nanling Mountains meet the Luoxiao Mountains and where the Yangtze River System separates from the Pearl River water system divide It has served as a strategic passageway from the Central Plains to the South China coast since ancient times. Chenzhou has a rich history dating back over 2200 years to the Qin Dynasty when counties were established along the Chitao Road. It is also one of the cradles of Chinese agricultural culture with profound cultural significance.
Chenzhou boasts a wealth of tourism resources due to its warm and humid climate, stunning mountains, and picturesque landscapes. Tourists can visit Dongjiang Lake, known as “Dongting in the south of Xiangnan”, Su Xianling with its rich Taoist culture, Wanhua Rock with its awe-inspiring “Caves of the World”, and Jiulongjiang National Forest Park with its “emerald screen” of green hills on all sides. Other attractions include Fei Tian Shan National Geological Park with its Dan Xia landforms; Mang Mountain with the largest primitive forest at the same latitude on earth, known as China’s “first mountain of primitive ecology” and hailed by scholars as a “biological gene pool” and “kingdom of flora and fauna”; Wugai Mountain, southern China’s only international hunting ground open to the public; and Banliang Ancient Village in southern Hunan Province, which offers tourists an authentic rural experience while also showcasing local history and culture
In 2014, Chenzhou received 33.43 million domestic and international tourists, and its total tourism revenue reached CNY 23.70 billion (approximately USD 3.85 billion), accounting for 12.8% of Chenzhou’s GDP that year; in 2019, Chenzhou received 80.06 million domestic and international tourists, and its total tourism revenue increased to CNY 68.76 billion (approximately USD 9.95 billion), accounting for 32.7% of Chenzhou’s GDP that year (Table 1). Tourism has become a pillar industry for Chenzhou’s economy.
2.2. Data Source
The number of domestic and international tourists to the city was acquired from the annual reports or statistics of Chenzhou’s travel agencies and culture and tourism bureaus. In-depth surveys were conducted in more than 10 travel agencies in the city to determine the origins of domestic and international tourists, travel distances, means of transportation used, length of stay, number of individuals participating in various tourism activities, and the average frequency of visits. The types and quantities of food offered to tourists as well as the number of people staying at various hotel categories were investigated by conducting in-depth surveys to a variety of restaurants and hotels in Chenzhou. All the in-depth surveys were conducted based on the database of the interviewed travel agencies, hotels, restaurants. The source of carbon emission coefficients of energy consumption is shown in Table 2.
2.3. Methods
In this study, the Life Cycle approach is used to examine tourism carbon footprints. According to ISO 14040:1999, Life Cycle Assessment (LCA) is a comprehensive analysis and assessment of all environmental consequences of products, processes, and services throughout their whole life cycle, “from cradle to grave”. This study is based on the following four steps.
-
(1). Define the objectives and scope of the LCA study. The objective of the study is to assess the environmental impact through the carbon footprint analysis of resources and energy consumed by the whole tourism activity; this study decomposes the tourism activity into seven different functional units (different tourism activities), such as transportation, catering, accommodation, sightseeing, shopping, entertainment and waste disposal, in terms of the expression of services provided by tourism activities. The boundary range of each different functional unit is defined by the pathways and the functional orientation of the product offered by the different functional units (different tourism activities) services.
-
(2). Inventory analysis. In this step, we first developed a life cycle model based on the objectives and the identified scope of the study. Then we collected the data of the resources and energy (products) of the direct and indirect services for each different functional unit, and then we calculated and summarized the results of the product life cycle inventory based on the collected data.
-
(3). Life cycle impact assessment. The inventory analysis was turned into specific impact types and indicator parameters, and the product life cycle environmental impact evaluation was conducted to define the product life cycle environmental impact and provide environmental data or information to support decision-making.
-
(4). Life cycle interpretation. The findings of the inventory analysis and the life cycle impact assessment were analyzed.
2.4. Tourism Carbon Footprint Calculation Model
This study calculated the tourism carbon footprint using a life-cycle approach based on multiple decisions (e.g., transportation modes, hotel levels, tourism activities) of tourists in a freer and more active context, which suited a “bottom-up” analysis path. This approach divides the carbon footprint of tourism into seven categories: transportation, catering, accommodation, sightseeing activities, shopping, entertainment activities, and waste disposal; analyzes their carbon footprints independently; and then sums them to determine the total carbon footprint of tourism.
2.4.1. Carbon Footprint Calculation Model for Tourism Transportation
Tourism transportation consists of two parts: “external transportation” (the transportation between a tourist’s home and his/her destination, including round trips) and “internal traffic” (the travel transportation within the tourism region). The tourism transportation of a tourist equals the sum of his/her external and internal transportation. The carbon footprint of tourism transportation can be calculated as follows:
C1 = Σ(N1 × A1k × Djj × fjk) + Σ(N2 × A2k × Dj × fj)(1)
where: C1 denotes the carbon footprint of tourism transport (106 kg); N1 and N2 represent the number of domestic and international tourists, respectively; A1k and A2k are the distances travelled per capita by domestic and international tourists (km), respectively; Dj denotes the carbon emission factor of different modes of transport (kg/(per person–per km)); fj denotes the correction factor of the carbon emission factor of different modes of transport; k = 1, 2 (external and internal transport); j = 1,. 2, 3, …, n (type of transportation: airplane, train, car, self-driving car, cab, …, n).2.4.2. Carbon Footprint Calculation Model for Tourism Catering
This study only considers the carbon footprint of food consumption by restaurants (i.e., energy resources consumed (input) to produce a unit output of food and carbon footprint of food processing process (energy consumption source during processing)); Equation (2) shows the carbon footprint estimation model of tourism restaurants. The estimated results may be lower than the actual ones because raw material preparation, food preparation, and mid-meal service energy consumption are not included.
C2 = Σ(Nk × Pkj × Hkj × Gkj × Rkj) + Σ(Nk × Lki × Tki × Gki × Yki)(2)
where: C2 indicates the carbon footprint of tourism catering (106 kg); NK indicates the number of tourists; Pkj denotes the amount of different grains, vegetables and meats consumed by each tourist for one breakfast (kg/per meal); Hkj is the amount of different grains, vegetables and meats consumed by each tourist for one mid/evening meal (kg/per meal); Gkj indicates the number of mid/evening meals and breakfasts per tourist; Rkj is the carbon emission factor for producing different units of grains, vegetables and meats (kg/kg); Lki indicates the amount of different energy used to process a single tourist’s breakfast food (kg); Tki indicates the amount of energy used to process a single tourist’s mid/evening meal (kg); Yki denotes the carbon emission factor of different energy used to process a tourist’s food per meal (kg/kg); k = 1, 2 (domestic and international tourists); j = 1, 2, …, n (rice, wheat, meats, vegetables, …, n); i = 1, 2, …, n (coal, natural gas, LPG, …, n).2.4.3. Carbon Footprint Calculation Model for Tourism Accommodation
The carbon footprint of tourism accommodation can be estimated by multiplying the number of tourists (Nj) staying in different types of hotels, the number of beds (Mji) per room in each hotel type, and the carbon emission coefficient (Eji) of energy consumption per room/per bed/per night for each hotel type.
C3 = ΣNj × Mji × Eji(3)
where: C3 denotes the carbon footprint of tourist accommodation (106 kg); j = 1, 2, 3, 4, …, n (star hotel, general hotel, resort, family hotel, n); i denotes the number of beds per room in the hotel, i = 1, 2, 3, 4.2.4.4. Carbon Footprint Calculation Model for Tourism Sightseeing Activities
The carbon footprint of tourism sightseeing activities can be obtained by multiplying the number of participants in each activity, the frequency of participation in each activity, and the carbon emission coefficients of energy consumption for different types of activities.
C4 = ΣMi × Ki × Si(4)
where: C4 denotes the carbon footprint of tourism sightseeing activities (106 kg); Mi denotes the number of tourists involved in activity i; Ki denotes the frequency of participants involved in activity i; Si denotes the energy consumption carbon emission factor (kg/per person) for activity i. i = 1, 2, …, n (forest park, nature sightseeing tour, …, n).2.4.5. Carbon Footprint Model for Tourism Shopping
As for shopping, tourists to Chenzhou usually buy local fruits and fish from Dongjiang Lake. Therefore, this study constructs the carbon footprint model of tourism shopping according to the method of calculating the carbon footprint of producing different fruits and fish (Equation (5)). What’s more, this study only calculates the carbon footprint of energy consumption in producing tourism-related products; it ignores the carbon footprint of energy consumption in the storage and sales management process of tourism-related products (its estimation result may be lower than the actual result).
C5 = ΣWj × Hj(5)
where: C5 denotes the carbon footprint of tourism shopping (106 kg); Wj is the consumption (kg) of tourism-related product j; Hj is the carbon emission factor (kg/kg) of energy consumption for producing 1 kg tourism-related product j.2.4.6. Carbon Footprint Calculation Model for Tourism Entertainment Activities
Tourism entertainment activities includes leisure and recreational activities that tourists engage in during their trip. Its carbon footprint calculation model is:
C6 = ΣEj × Qj(6)
where: C6 denotes the carbon footprint of tourism entertainment activities (106 kg); Ej denotes the number of people in entertainment activity j; Qj denotes the carbon emission factor (kg/per person) of energy consumption of entertainment activity j.2.4.7. Carbon Footprint Calculation Model for Tourism Waste Disposal
This study only considered solid waste from tourism because of the difficulty in obtaining data on liquid waste. The amount of tourism-related solid waste was estimated using a method from the literature [34] based on the length of stay of each tourist. It was assumed that each tourist generated the same amount of solid waste per day. It was also assumed that all solid waste was disposed of in sanitary landfills. The carbon footprint of sanitary landfill waste consisted of two parts: the carbon footprint of energy consumption for transporting tourism waste from the source to the landfill, which was the only part calculated in this study, and the carbon footprint of energy consumption in landfills, which was omitted. Therefore, the estimation result might be lower than the actual value. The actual results could differ from the estimated ones; and the amount of CH4 emission from sanitary landfill waste, which was calculated according to IPCC 1995.
C7 = Σ (M × P) + Σ(M × E × K × L × 0.5 × T)(7)
where: C7 denotes the carbon footprint of tourism waste disposal (106 kg); M denotes the waste production (kg); P denotes the carbon emission coefficient of landfill waste (kg/kg) K denotes the percentage of degradable organic carbon in landfill waste (%); L denotes the decomposition rate of degradable organic carbon in landfill denotes the decomposition rate of degradable organic carbon in landfill; T denotes the greenhouse effect value of CH4 relative to CO2 [35].Adding up the carbon footprints of the above tourism sub-projects gives the total carbon footprint of tourism (Ct), i.e.,
Ct = C1 + C2 + C3 + C4 + C5 + C6 + C7(8)
3. Results
3.1. Carbon Footprint of Tourism Transportation
The annual carbon footprint of tourism transportation in Chenzhou from 2014 to 2019 ranged from 76.8809 to 194.6067 × 106 kg (Table 3). International and domestic tourists ac-counted for 5.45–7.17% and 92.83–94.51% of the total carbon footprint of tourism transportation, respectively. This was because international tourists comprised only 0.60–0.79% of domestic tourists visiting Chenzhou each year.
Chenzhou’s tourism transportation carbon footprint increased annually from 2014 to 2019 (average annual increase of 21.15%), but domestic tourism transportation carbon footprint increased faster (21.53%) than international tourism transportation carbon footprint (12.42%). This can be explained by three factors: first, changes in travel distance for tourists due to reorganization of tourism resources in 2017 (Table 3); second, changes in tourists’ choice of transportation modes, with more domestic tourists opting for self-driving, which has a CO2 emission intensity 1.53 times higher than that of buses (Table 3); and third, an average annual growth of 19.58% in tourist arrivals, which is comparable to the average annual growth in transportation carbon footprint (21.15%). Therefore, both the annual variation in Chenzhou’s tourism transportation carbon footprint and the growth in tourist arrivals play crucial roles.
3.2. Carbon Footprint of Tourism Catering
Although catering involves energy consumption, this study considers it negligible because most of the food served to tourists in Chenzhou is locally produced. Table 4 shows that international tourists have higher carbon footprints for breakfast and lunch/dinner than domestic tourists. International tourists prefer milk and bread for breakfast, while domestic tourists usually eat noodles, steamed buns, sauerkraut, and porridge. The carbon footprints of individual meals for domestic and international tourists differ depending on the energy required for producing and processing various foods.
Table 4 also shows that both domestic and international tourists had a stable individual meal carbon footprint over time. Each tour group in Chenzhou followed a predetermined route and visited a fixed number of tourist restaurants. The processing procedure used in each restaurant was fixed, so there were few significant differences in the carbon impact of individual meals.
The carbon footprint of tourism catering in Chenzhou ranged from 487.63 to 1321.91 × 106 kg from 2014 to 2019, of which 481.57 to 1307.18 × 106 kg came from domestic tourists, accounting for 98.76% to 99.02% of the total, while 6.06 to 14.73 × 106 kg came from international tourists and only 0.98% to 1.24% of the total. It is evident that domestic tourists were responsible for almost all the carbon impact of Chenzhou’s tourism catering.
The catering carbon footprint of international tourists grew at a lower annual rate (19.68%) than that of domestic tourists (23.34%). This was mainly due to the increasing number of tourists (21.01% annually), rather than changes in individual meal carbon footprint or meals per capita. The expansion of tourism resources in 2017 also contributed to the growth of catering carbon footprint.
3.3. Carbon Footprint of Tourism Accommodation
Domestic tourists stayed 3 nights on average while international tourists stayed 4 nights on average in Chenzhou, and the carbon footprint of accommodation per capita for international tourists is 3.40–3.52 times that of domestic tourists (compare Table 5 and Table 6). First, because international tourists spent one more night in accommodations than domestic tourists on average, their per capita carbon footprint of accommodation is greater than that of domestic tourists. Second, and more importantly, due to their diverse habits and social status, their preferences for living conditions vary. International tourists chose star hotels (80–85%), general hotels (6.0–6.5%), resorts (5.0–8.6%), and almost no homestay; they also preferred one single room for one person (87.92–95.89%) over two people (4.11–12.08%) (see Table 5). In contrast, domestic tourists chose star-rated hotels (5.5–6.2%), general hotels (80–82%), resorts (5.0–6.5%), family hotels (6.5–10%); they mostly stayed in one single room for two people (95–96%) (Table 5). The carbon footprint of energy consumption per room, per bed, and per night varied for different types of hotels: star hotels > resorts > general hotels > family hotels [22,23].
The annual carbon footprint of tourism accommodation in Chenzhou fluctuated between 0.73 and 1.75 Tg CO2e/year in the period of 2014–2019, with domestic tourists contributing 97.3–97.9% and international tourists contributing 2.0–2.7%. Despite emit-ting more carbon per capita than domestic tourists, international tourists constituted only 0.6–0.7% of the total tourist arrivals, which accounts for the substantial discrepancy between their respective carbon footprints of tourism accommodation.
The carbon footprint of accommodation per capita for domestic and international tourists increased by 0.56% and 0.15% annually from 2014 to 2019, respectively. This indicates that the proportion of tourists staying in different types of hotels remained relatively stable over the years. The total carbon footprint of tourism accommodation in Chenzhou grew by 19.56% annually on average, driven by the increasing number of tourists (19.58% annual growth).
3.4. Carbon Footprint of Tourism Sightseeing Activities
Chenzhou sightseeing activities include forest parks, nature tours, theme parks (such as Shennong Square that showcases ancient Chinese farming culture), and cultural landscapes (such as revolutionary sites). Some tourists visited multiple attractions within the same category of sightseeing activities. For example, some tourists went to Su Xian Ling and the Dragon Lady, both of which are nature tours; some others went to Mang Mountain National Forest Park and Jiulong River National Forest Park, both of which are forest park tours. However, there is no difference in the carbon footprint of sight-seeing activities between international and domestic tourists who visited the same at-tractions.
International tourists have a higher carbon footprint of sightseeing activities per capita than domestic tourists, because they stayed longer and visited more attractions. However, the total carbon footprint of sightseeing activities for international tourists is much lower than that for domestic tourists, because they account for only about 1% of the annual tourist population.
The carbon footprint of sightseeing activities in Chenzhou ranged from 90.60 × 106 kg to 203.73 × 106 kg (Table 7) between 2014 and 2019, with domestic tourists contributing most of it. The carbon footprint of sightseeing activities in Chenzhou increased by 26.28% annually, and the growth rate of domestic tourists’ carbon footprint (26.35%) was higher than that of international tourists’ (18.83%). This suggests that the annual variation of the carbon footprint of sightseeing activities in Chenzhou was mainly driven by the domestic tourists’ behavior.
3.5. Carbon Footprint of Tourism Entertainment Activities
Tourism entertainment activities are optional and usually not arranged by travel agents due to the extra cost. International and domestic tourists have the same carbon footprint for the same entertainment activities, as they do for general sightseeing activities.
The carbon footprint of chess and card entertainment facilities was counted in the carbon footprint of tourist accommodations for all hotel types. The main entertainment activities in Chenzhou are cruise trips in Dongjiang Lake, yacht surfing, and hot spring bathing. Tourists participated in each activity only once, because travel agencies did not arrange repeated visits to Dongjiang Lake in the same tour, and hot springs depend on the tourists’ travel time and budget.
Although yacht surfing has a carbon footprint coefficient per person that is 6.83 times higher than hot springs and 218.8 times higher than cruise ship excursions [11], domestic tourists favored the latter two activities, resulting in a larger carbon footprint than yacht surfers. Conversely, international tourists tend to choose yacht surfing and hot springs, making yacht surfing the activity with the highest carbon footprint.
The carbon footprint of tourism entertainment activities in Chenzhou increased from 29.90 × 106 kg to 91.62 × 106 kg (Table 8) between 2014 and 2019, with domestic and international tourists contributing 87.42% to 91.90% and 8.10% to 12.58%, respectively. Despite the high participation rate of international tourists in entertainment activities (more than 90% each year), their carbon footprint was much lower than that of domestic tourists, who outnumbered them by about 99 times each year. However, only less than 15% of domestic tourists engaged in hot springs, and even fewer in yacht surfing. The annual growth rate of the carbon footprint of tourism entertainment activities in Chenzhou was 25.46%, but it was higher for domestic tourists (26.75%) than for inter-national tourists (14.75%).
3.6. Carbon Footprint of Tourism Shopping
Local handicrafts made from plants and local fruits and seafood from Dongjiang Lake are some of the special products in Chenzhou. However, these products are not mass-produced and data on their purchase by tourists are scarce (this study excludes handicrafts purchase due to data unavailability). International tourists rarely shop for local specialties to avoid customs issues; only 7% to 9% of domestic tourists purchased them annually.
Table 9 shows that the annual carbon footprint of tourism shopping in Chenzhou increased from 4.17 × 106 kg to 10.66 × 106 kg between 2014 and 2019. Fruits purchase con-tributed to 99.29% of the average yearly carbon footprint of tourism shopping, while other products such as chestnuts (Castaneamollissima) and dried fish accounted for only 0.71%. Chenzhou has a well-developed fruit industry and produces high-quality fruits such as alpine yellow peaches (Amygdalus persica) and snow pears (Echeveria ‘Sulli’), which attract many tourists. The carbon footprint of tourism shopping in Chenzhou grew by 22.26% annually due to the increase of domestic tourists.
3.7. Carbon Footprint of Tourism Waste Disposal
The annual carbon footprint of tourism waste disposal in Chenzhou increased from 69.14 × 106 kg to 171.60 × 106 kg between 2014 and 2019. The carbon footprint of landfill accounted for 3.15% to 3.47%, while the carbon footprint of CH4 emissions from landfill accounted for 98.53% to 96.85% (Table 10). The carbon footprint of CH4 emissions from landfill was 27.81 to 30.74 times higher than that of landfill, because CH4 has a green-house effect value that is 21 times higher than that of CO2 [36].
Domestic tourists contributed 99.06% to 99.24% of the carbon footprint of tourism waste disposal in Chenzhou, while international tourists contributed only 0.76% to 0.94% (Table 10). Assuming that each tourist (domestic or international) generated the same amount of solid waste per day, this implies that the number of international tourists in Chenzhou tourism was only 0.7% to 0.9% of the total number of tourists each year, leading to a much higher carbon footprint of tourism waste disposal for domestic tourists than for international tourists.
The carbon footprint of tourism waste disposal in Chenzhou increased from 60.14 × 106 kg in 2014 to 171.60 × 106 kg in 2019, with an average annual increase of 19.67%. Assuming that each tourist discarded the same amount of solid waste per day, this indicates that the carbon footprint of waste disposal for both domestic and international tourists increases at the same rate as their number grows each year.
3.8. Total Carbon Footprint of Tourism
From 2014 to 2019, the carbon footprint of Chenzhou tourism fluctuated from 9085.28 × 106 kg to 23,009.42 × 106 kg, with domestic tourists accounting for 93.64% to 95.12% and international tourists contributing 4.8% to 6.3% (Table 11). The majority of Chenzhou tourism carbon footprint is generated by domestic tourists.
The carbon footprint composition of domestic tourists in transportation, accommodation, catering, sightseeing activities, entertainment activities, shopping, and tourism waste disposal accounted for 82.84–84.07%, 8.09–8.56%, 5.661–6.42%, 0.85–0.92%, 0.31–0.33%, 0.05–0.06%, and 0.69–0.87% respectively; for international tourists, these values were 93.55–94.88%, 3.27–3.45%, 1.01–1.20%, 0.11–0.12%, 0.60–0.69%, 0, and 0.11–0.12%. It is evident that transportation contributed more to the carbon footprint of international tourists than domestic tourists, while domestic tourists had higher proportions of carbon footprint from all other tourism activities than international tourists.
As shown in Table 11, the carbon footprint of tourism in Chenzhou increased annually from 2014 to 2019 by an average of 20.89%, with the average annual growth for domestic tourists being 21.01% and the average annual increase for international tourists being 17.00%. It demonstrates that domestic tourists have a greater impact on the annual change of Chenzhou tourism carbon footprint than international tourists.
4. Discussion
The majority of tourism carbon footprint in Chenzhou is attributable to transportation, which is consistent with other studies [37]. This is because tourists to their destinations need to travel by airplanes, trains, vehicles, and other energy-intensive modes of transportation, and the longer the distance traveled to and from the destination, the more CO2 is emitted. According to some experts, the carbon footprint of tourism is essentially the carbon footprint of airlines, trains, vehicles, and other modes of transportation [38].
With the gradual increase in the popularity of Chenzhou tourist attractions, tourism service standards and management systems have gradually improved, attracting an increasing number of tourists. Although it increases tourism revenue, the arrival of a large number of tourists induces more energy demand, which translates into higher carbon emission levels, making Chenzhou’s tourism carbon footprint increase rapidly from 2014 to 2019. Tourism will not be sustainable if its carbon footprint exceeds its carbon capacity. To ensure the sustainability of tourism, it requires measures such as energy conservation and low carbon tourism to reduce the tourism carbon footprint; increasing the amount of vegetation in tourist destinations to improve the carbon sink capacity; and increasing the ticket price and environmental taxes to increase tourism costs in order to limit the number of tourists (from an economic standpoint, when the cost of tourism increases, the number of tourists tends to decrease).
Chenzhou’s carbon footprint per capita is lower than that of Gansu Province [16], Shandong Province [18,21], Jiujiang City, Jiangxi Province [22], and Hainan Province [30] in China, and higher than that of Jigongshan, Henan Province [17], Jiangsu Province [10], Liaoning Coastal Economic Zone [15], Yunnan Province [19], Yangtze River Delta [20], and Wuhan City, Hubei Province [38] in China, and the results of these studies vary considerably. Since there is no unified definition and connotation of carbon footprint [39], the connotation and extension of the concept of tourism carbon footprint, statistical data source and calculation method, boundary of measurement system, and access to data channels are still inconsistent among different research. Some scholars calculate the carbon footprint of transportation only within the scenic area, ignoring that outside the scenic area [17,18,33]; some studies base the carbon footprint of tourism catering on the eating habits of local residents [30,33], while some obtain it through surveys of various types of restaurants [19]; some scholars use the number of tourist housing by dividing the annual number of overnight tourists by the annual average room occupancy rate [38], while some use the housing rate [17], or the number of rooms and the number of tourists choosing different classes of hotels and one-person and two-person occupancy (as in this study) are used to obtain the carbon footprint. The tourism carbon footprint comprises both direct and indirect carbon footprints, but some research ([17,19,33,38] and this study) disregard the indirect carbon footprint. Some scholars have expanded the calculation of tourism carbon footprint from “catering, accommodation, transportation, sightseeing, shopping and entertainment” to include “water use” and “waste disposal” throughout the entire process of tourism product manufacturing and consumption [33]. Most studies only examine the carbon footprint of “catering, accommodation, transportation, and sightseeing”, ignoring the carbon footprint of “shopping, entertainment, and waste disposal”. Basic data for evaluating the tourism carbon footprint at a provincial and municipal levels are mainly from administrative statistics, whilst data for specific scenic spots are mostly obtained through in-depth surveys based on the actual tourist consumption. Due to these factors, the results of various studies vary from one another. The quantitative calculation of tourism carbon footprint is based on the quantitative assessment model of tourism carbon footprint, which demands not only consistent statistical caliber but also operability. (1) The principle of consumption traceability. The direct carbon footprint of a country or a region is emphasized in the calculation of the carbon footprint of tourism goods or services, while the indirect carbon footprint of the outside is disregarded. Therefore, the carbon footprint calculation of tourists should adhere to the principle that the one who consumes is responsible for the carbon footprint so as to take full responsibility for tourists and prevent carbon leakage. (2) The principle of clear boundary. The carbon dioxide produced by the direct and deep modes of carbon footprint is produced anywhere, but tourist consumption should be included in the calculation of carbon footprint. Therefore, the study of tourism carbon footprints should clarify the boundary of “catering, accommodation, transportation, sightseeing, shopping, entertainment and waste disposal”. Research shows that the estimation results of the “top-down” method and the “bottom-up” method are generally consistent, provided that the system boundaries are consistent [11]. (3) The principle of regional sharing. Developed countries or regions may achieve carbon emission reduction through the outsourcing of products or services, using carbon dioxide intensive products in developing countries and regions. in accordance with the principle of fairness and justice of greenhouse gas emissions, all the tourist destinations involved in a tourism itinerary should be treated equally and share the carbon footprint of tourists.
The tourism carbon footprint is a complex system that varies from region to region and from sector to sector of the tourism industry. There are a number of factors that influence tourism carbon footprints, including time, space, consumption, policy, and technology. (1) To extend the average length of stay under the condition of constant traffic and other conditions will result in greater tourism carbon footprints, because the longer the stay, the greater the tourism consumption will be. So, optimizing tourism routes and reducing the length of stay are effective measures to reduce tourism carbon footprints. (2) In general, the greater the distance between the source place of tourists and their destinations, the greater the energy consumption of tourist transportation and tourism carbon footprint will be. The key to reducing tourism carbon footprint is to replace transportation with a high carbon emission coefficient with transportation with a lower carbon emission coefficient. (3) Tourism consumption is associated with tourists’ view on consumption, tourists’ income, and the abundance of tourism products. Generally speaking, the higher the average consumption level of tourists, the greater the tourism carbon footprint will be. Therefore, directing the “rational consumption” of tourists is an important step in reducing the carbon footprint of tourism. (4) Policy factors, including primarily government policies on tourism consumption, energy conservation and emission reduction, low carbon tourism, tourism shopping, and market development, etc. Governments at all levels should develop appropriate strategies that can both reduce tourism carbon footprint and promote local economic development according to local conditions. (5) Technical factors mainly include the application of energy-saving equipment and low-carbon technology. The use of energy-saving equipment and the application of low-carbon technology should be strengthened, making significant technical efforts to reduce tourism carbon footprint.
5. Conclusions
From 2014 to 2019, the carbon footprint of domestic tourists accounted for 93.70–95.10% (as opposed to 4.90–6.30% for international tourists) of the carbon footprint of tourism in Chenzhou. For domestic tourists, their carbon footprint is composed of transportation, accommodation, catering, sightseeing, entertainment, waste disposal, and shopping in descending order, while for international tourists, the order is transportation, accommodation, catering, sightseeing, entertainment, waste disposal, and shopping. Typically, for a specific urban tourism destination like Chenzhou, the vast majority of the tourism carbon footprint of the place is generated by domestic tourists, and it is mainly composed of transportation carbon footprint.
The tourism carbon footprint in Chenzhou increases at an average yearly rate of 20.89%, with an average annual increase of 21.01% for domestic tourists and 17% for international tourists. Domestic tourists are primarily responsible for the increase of Chenzhou’s tourism carbon footprints. The following is a ranking of the average annual growth in carbon footprints for each Chenzhou tourism component: entertainment, catering, shopping, sightseeing, transportation, waste disposal and accommodation. Since transportation carbon footprint accounts for the vast majority of the city’s overall tourism carbon footprint, the change in the amount of transportation carbon footprints will largely determine the change in the amount of the overall tourism carbon footprint. The annual per-person carbon footprints of accommodation, sightseeing activities and waste disposal fluctuate less rapidly. It shows that the service standards and management system of the Chenzhou tourism business for accommodation, catering, and entertainment activities have improved. Meanwhile, the annual change of carbon footprints of catering, entertainment activities, and shopping is greater due to the uncertainties of these consumption activities. In sum, to lower the tourism carbon footprint in Chenzhou, we must optimize tourism resources, shorten transportation distances, and replace high-carbon-emission transportation modes with low-carbon-emission ones. For sustainable tourism development in Chenzhou, we must establish appropriate strategies to conserve energy, promote low-carbon tourism, and reduce carbon footprints from other tourism activities.
Although this study more precisely captures the structural characteristics, current status, and development changes of Chenzhou’s tourism carbon footprints, it is not without limitations. First, this study only measures the carbon emissions of products directly consumed by tourists (direct carbon footprint), disregarding the carbon footprint of indirect services for the consumption process of these products (indirect carbon footprint). For example, the carbon footprint of raw material preparation and meal service was omitted when calculating the carbon footprint of tourism catering; the carbon footprint of tourism product transportation, storage, and sales management was omitted when calculating the tourism carbon footprint, and so on. Second, due to the difficulty of obtaining some data, the carbon footprint of casual tourism that does not participate in group tours (for which information and statistics are not available) was omitted. Realistic results and analysis can only be achieved by developing a tourism satellite account based on the system boundaries of tourism and then calculating the tourist carbon footprint.
Conceptualization, Q.X. and Y.Z.; Data curation, Q.X., Y.Z. and J.D.; Methodology, Q.X. and Y.Z.; Supervision, Y.Z. and J.D.; Validation, Y.Z.; Writing—original draft, Q.X.; Writing—review and editing, Y.Z. and J.D. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Please contact the authors via email for the data.
Thanks to the Chenzhou Municipal People’s Government, travel agencies in Chenzhou and other related open platforms for the support.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Number of Tourists and Tourism Revenue in Chenzhou from 2014 to 2019.
| Year | DT Number |
IT Number |
Total Number of Tourists |
IT Revenue |
DT Revenue |
Total Tourism Revenue |
|---|---|---|---|---|---|---|
| 2014 | 33.166 | 264 | 33,430 | 0.7316 | 22.9724 | 23.7040 |
| 2015 | 38.282 | 296 | 38,578 | 0.8355 | 27.5178 | 28.3533 |
| 2016 | 43.297 | 309 | 43,606 | 0.8883 | 31.1589 | 32.0472 |
| 2017 | 61.275 | 371 | 61,645 | 1.0788 | 46.9178 | 47.9966 |
| 2018 | 71.618 | 449 | 72,067 | 1.3603 | 59.4481 | 60.8085 |
| 2019 | 79.513 | 549 | 80,062 | 1.6738 | 67.0885 | 68.7624 |
Note: DT = domestic tourists; IT = international tourists. Data Source: the 2014–2019 statistical reports from Chenzhou City Bureau of Culture and Tourism.
Source of carbon emission coefficients of energy consumption.
| Carbon Emission Coefficients of Energy Consumption | Source |
|---|---|
| Different means of transportation | Gössling [ |
| Food and processing of tourists’ meals | Tan Qucheng [ |
| One room per person per night in different grades of hotels | Gössling [ |
| Tourism activities | Becken [ |
| Tourism shopping | Zhen Yi [ |
| Tourism waste disposal | The empirical formula recommended by IPCC in 1995 |
Carbon footprint of tourism transportation in Chenzhou from 2014 to 2019.
| Year | Tourist Type | External Transportation | Internal Transportation | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Transportation Mode | Number of Tourists |
Distance per Capita |
CF per Capita |
Total CF |
Transportation Mode | Number of Tourists |
Distance per Capita |
CF per Capita |
Total CF |
||
| 2014 | IT | Airplane | 26.3514 | 12,784 | 2051.83 | 540.69 | Bus | 25.2973 | 321 | 25.28 | 6.40 |
| Train | 26.3514 | 634 | 16.64 | 4.38 | Taxi | 1.0541 | 334 | 53.66 | 0.57 | ||
| Subtotal | 26.3514 | 2068.47 | 545.07 | Subtotal | 26.3514 | 26.45 | 6.97 | ||||
| CF per capita 2094.92 kg, Total CF 552.04 × 106 kg | |||||||||||
| DT | Airplane | 862.32 | 3052 | 489.95 | 4224.94 | Bus | 3084.45 | 331 | 26.06 | 803.81 | |
| Train | 3217.11 | 2187 | 57.41 | 1846.90 | Self-driving | 99.50 | 387 | 46.73 | 46.50 | ||
| Self-driving | 99.50. | 1364 | 164.70 | 163.88 | Other | 132.66 | 359 | 43.34 | 57.49 | ||
| Subtotal | 3316.61 | 188.02 | 6235.72 | Subtotal | 3316.61 | 27.37 | 907.80 | ||||
| CF per capita 215.37 kg, Total CF 7143.52 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 7688.09 × 106 kg | |||||||||||
| 2015 | IT | Airplane | 29.5986 | 13,028 | 2091.00 | 618.91 | Bus | 28.7106 | 318 | 25.04 | 7.19 |
| Train | 29.5986 | 634 | 16.64 | 4.93 | Taxi | 0.8880 | 325 | 52.21 | 0.46 | ||
| Subtotal | 29.5986 | 2107.64 | 623.84 | Subtotal | 29.5986 | 25.84 | 7.65 | ||||
| CF per capita 2133.48 kg, Total CF 631.49 × 106 kg | |||||||||||
| DT | Airplane | 1033.61 | 3173 | 509.27 | 5263.87 | Bus | 3483.66 | 324 | 25.52 | 889.03 | |
| Train | 3636.79 | 2056 | 53.97 | 1962.77 | Self-driving | 191.41 | 372 | 44.92 | 85.98 | ||
| Self-driving | 191.41 | 1394 | 168.32 | 322.18 | Other | 153.13 | 347 | 41.90 | 64.16 | ||
| Subtotal | 3828.20 | 197.19 | 7548.82 | Subtotal | 3828.20 | 27.15 | 1039.17 | ||||
| CF per capita 224.33 kg, Total CF 8587.99 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 9219.48 × 106 kg | |||||||||||
| 2016 | IT | Airplane | 30.9239 | 13,168 | 2113.46 | 653.56 | Bus | 30.3054 | 326 | 25.67 | 7.78 |
| Train | 30.9239 | 634 | 16.64 | 5.14 | Taxi | 0.6185 | 337 | 52.53 | 0.33 | ||
| Subtotal | 30.9239 | 2130.10 | 658.70 | Subtotal | 30.9239 | 26.23 | 8.11 | ||||
| CF per capita 2156.33 kg, Total CF 666.81 × 106 kg | |||||||||||
| DT | Airplane | 1169.02 | 3056 | 490.49 | 5733.92 | Bus | 3810.14 | 329 | 25.91 | 987.20 | |
| Train | 3983.33 | 2130 | 55.91 | 2227.80 | Self-driving | 346.38 | 366 | 44.19 | 153.07 | ||
| Self-driving | 346.37 | 1407 | 169.89 | 588.45 | Other | 173.19 | 351 | 42.38 | 73.40 | ||
| Subtotal | 4329.71 | 204.41 | 8550.17 | Subtotal | 4329.71 | 28.03 | 1213.67 | ||||
| CF per capita 225.51 kg, Total CF 9763.48 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 10,230.29 × 106 kg | |||||||||||
| 2017 | IT | Airplane | 36.9926 | 12,861 | 2064.19 | 763.59 | Bus | 36.2527 | 390 | 30.72 | 11.16 |
| Train | 36.9926 | 634 | 16.64 | 6.16 | Taxi | 0.7399 | 431 | 69.24 | 0.51 | ||
| Subtotal | 36.9926 | 2080.83 | 769.75 | Subtotal | 36.9926 | 31.54 | 11.67 | ||||
| CF per capita 2112.37 kg, Total CF 781.42 × 106 kg | |||||||||||
| DT | Airplane | 1351.86 | 3174 | 509.47 | 6887.32 | Bus | 5330.90 | 378 | 29.77 | 1587.01 | |
| Train | 5576.00 | 2214 | 58.12 | 3240.77 | Self-driving | 551.47 | 389 | 46.97 | 259.02 | ||
| Self-driving | 551.47 | 1365. | 164.82 | 908.93 | Other | 245.10 | 381 | 46.01 | 112.77 | ||
| Subtotal | 6127.47 | 180.12 | 11,037.02 | Subtotal | 6127.47 | 31.86 | 1958.80 | ||||
| CF per capita 212.09 kg, Total CF 12,995.82 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 13,777.24 × 106 kg | |||||||||||
| 2018 | IT | Airplane | 44.9149 | 13,096 | 2101.91 | 944.07 | Bus | 44.0165 | 387 | 30.47 | 13.41 |
| Train | 44.9149 | 634 | 16.64 | 7.47 | Taxi | 0.8984 | 428 | 68.76 | 0.62 | ||
| Subtotal | 44.9149 | 2118.55 | 951.54 | Subtotal | 44.9149 | 31.24 | 14.03 | ||||
| Carbon footprint per capita 2149.79 kg, Total Carbon Footprint 965.57 × 106 kg | |||||||||||
| DT | Airplane | 1862.07 | 3213 | 515.68 | 9602.32 | Bus | 6230.77 | 375 | 29.53 | 1839.95 | |
| Train | 6374.01 | 2189 | 57.46 | 3662.51 | Self-driving | 787.80 | 390 | 47.89 | 370.97 | ||
| Self-driving | 787.80 | 1246 | 150.45 | 1185.25 | Other | 143.24 | 384 | 46.37 | 66.42 | ||
| Subtotal | 7161.81 | 201.77 | 14,450.08 | Subtotal | 7161.81 | 31.80 | 2277.43 | ||||
| CF per capita 233.56 kg, Total CF 16,727.51 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 17,693.08 × 106 kg | |||||||||||
| 2019 | IT | Airplane | 54.9374 | 12,996 | 2085.86 | 1145.91 | Bus | 53.8387 | 381 | 30.00 | 16.15 |
| Train | 54.9374 | 634 | 16.64 | 9.14 | Taxi | 1.0987 | 433 | 69.56 | 0.76 | ||
| Subtotal | 54.9374 | 2102.50 | 1155.05 | Subtotal | 54.9374 | 30.78 | 16.91 | ||||
| CF per capita 2133.28 kg, Total CF 1171.96 × 106 kg | |||||||||||
| DT | Airplane | 1987.80 | 3188 | 511.67 | 10,170.97 | Bus | 6838.05 | 380 | 29.92 | 2045.94 | |
| Train | 6997.07 | 2215 | 58.14 | 4068.10 | Self-driving | 954.15 | 392 | 47.33 | 451.60 | ||
| Self-driving | 954.15 | 1283 | 154.92 | 1478.17 | Other | 159.02 | 385 | 46.49 | 73.93 | ||
| Subtotal | 7951.22 | 197.67 | 15,717.24 | Subtotal | 7951.22 | 32.34 | 2571.47 | ||||
| CF per capita 230.01 kg, Total CF 18,288.71 × 106 kg | |||||||||||
| Total CF of Tourism Transportation 19,460.67 × 106 kg | |||||||||||
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint; Other means motorcycles, electric bicycles and other forms of transportation.
Carbon footprint of tourism catering in Chenzhou from 2014 to 2019.
| Year | Tourist Type | CF of M |
M per Capita | Total CF of M per Capita |
CF of B |
B per Capita | Total CF of B per Capita |
Total CF per Capita |
Number of People |
Total CF |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 | DT | 1.92 | 6 | 11.52 | 1.00 | 3 | 3.00 | 14.52 | 3316.61 | 481.57 |
| IT | 2.57 | 7 | 17.99 | 1.25 | 4 | 5.00 | 22.99 | 26.3514 | 6.06 | |
| Total | 3342.9614 | 487.63 | ||||||||
| 2015 | DT | 1.92 | 6 | 11.52 | 1.00 | 3 | 3.00 | 14.52 | 3828.20 | 555.85 |
| IT | 2.57 | 7 | 17.99 | 1.25 | 4 | 5.00 | 22.99 | 29.5986 | 6.80 | |
| Total | 3857.7986 | 562.65 | ||||||||
| 2016 | DT | 1.92 | 6 | 11.52 | 1.00 | 3 | 3.00 | 14.52 | 4329.71 | 628.27 |
| IT | 2.57 | 7 | 17.99 | 1.25 | 4 | 5.00 | 22.99 | 30.9239 | 7.11 | |
| Total | 4360.6339 | 635.38 | ||||||||
| 2017 | DT | 1.92 | 7 | 13.44 | 1.00 | 3 | 3.00 | 16.44 | 6127.47 | 1007.36 |
| IT | 2.57 | 8 | 20.56 | 1.25 | 5 | 6.25 | 26.81 | 36.9926 | 9.91 | |
| Total | 6164.4626 | 1017.27 | ||||||||
| 2018 | DT | 1.92 | 7 | 13.44 | 1.00 | 3 | 3.00 | 16.44 | 7161.81 | 1177.40 |
| IT | 2.57 | 8 | 20.56 | 1.25 | 5 | 6.25 | 26.81 | 44.9149 | 12.04 | |
| Total | 7206.7249 | 1189.44 | ||||||||
| 2019 | DT | 1.92 | 7 | 13.44 | 1.00 | 3 | 3.00 | 16.44 | 7951.22 | 1307.18 |
| IT | 2.57 | 8 | 20.56 | 1.25 | 5 | 6.25 | 26.81 | 54.9374 | 14.73 | |
| Total | 8006.1574 | 1321.91 |
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint; M = a mid/evening meal; B = breakfast.
Carbon footprint of domestic tourists’ accommodation in Chenzhou from 2014 to 2019.
| Year | Type of Accommodation | Accommodation Carbon Footprint (kg/kg) | Total Number of People (104) | Total CF (106 kg) | |||
|---|---|---|---|---|---|---|---|
| Number of People in a Room for One (104) | CF (106 kg) | Number of People in a Room for Two (104) | CF (106 kg) | ||||
| 2014 | Star-rated hotels | 9.29 | 5.74 | 156.54 | 48.37 | 165.83 | 54.11 |
| General hotels | - | - | 2653.29 | 557.19 | 2653.29 | 557.19 | |
| Resorts | 9.29 | 3.99 | 156.54 | 33.58 | 165.83 | 37.57 | |
| Homestay | - | - | 331.66 | 62.19 | 331.66 | 62.19 | |
| Subtotal | 18.58 | 3298.03 | 3316.61 | 711.06 (21.44 kg) | |||
| Ratio of type | Star-rated hotels 5%, General hotels 80%, Resorts 5%, Homestay 10% | ||||||
| 2015 | Star-rated hotels | 9.57 | 5.91 | 181.84 | 56.19 | 191.41 | 62.10 |
| General hotels | - | - | 3139.12 | 659.21 | 3139.12 | 659.21 | |
| Resorts | 9.57 | 4.10 | 181.84 | 39.00 | 191.41 | 43.10 | |
| Homestay | - | - | 306.26 | 57.27 | 306.26 | 57.27 | |
| Subtotal | 19.14 | 3809.06 | 3828.20 | 821.68 (21.46 kg) | |||
| Ratio of type | Star-rated hotels 5%, General hotels 82%, Resorts 5%, Homestay 8% | ||||||
| 2016 | Star-rated hotels | 16.67 | 10.30 | 221.46 | 68.43 | 238.13 | 78.73 |
| General hotels | - | - | 3493.77 | 733.69 | 3463.77 | 733.69 | |
| Resorts | 13.00 | 5.58 | 246.78 | 52.93 | 259.78 | 58.51 | |
| Homestay | - | - | 368.03 | 69.00 | 368.03 | 69.00 | |
| Subtotal | 29.67 | 4300.04 | 4329.71 | 939.93 (21.71 kg) | |||
| Ratio of type | Star-rated hotels 5.5%, General hotels 80%, Resorts 6%, Homestay 8.5% | ||||||
| 2017 | Star-rated hotels | 21.32 | 13.18 | 334.07 | 103.22 | 355.39 | 116.40 |
| General hotels | - | - | 4963.25 | 1042.28 | 4963.25 | 1042.28 | |
| Resorts | 18.99 | 8.15 | 360.92 | 77.42 | 379.91 | 85.57 | |
| Homestay | - | - | 428.92 | 80.42 | 428.92 | 80.42 | |
| Subtotal | 40.31 | 6087.16 | 6127.47 | 1342.67 (21.62 kg) | |||
| Ratio of type | Star-rated hotels 5.8%, General hotels 81%, Resorts 6.2%, Homestay 7% | ||||||
| 2018 | Star-rated hotels | 17.76 | 10.98 | 426.27 | 131.71 | 444.03 | 142.69 |
| General hotels | - | - | 5719.45 | 1201.08 | 5729.45 | 1201.08 | |
| Resorts | 26.14 | 11.21 | 496.67 | 106.54 | 522.81 | 117.75 | |
| Homestay | - | - | 465.52 | 87.29 | 465.52 | 87.29 | |
| Subtotal | 43.90 | 7117.91 | 7161.81 | 1548.81 (21.63 kg) | |||
| Ratio of type | Star-rated hotels 6.2%, General hotels 80%, Resorts 7.3%, Homestay 6.5% | ||||||
| 2019 | Star-rated hotels | 16.65 | 10.29 | 420.67 | 129.99 | 437.32 | 140.28 |
| General hotels | - | - | 6209.15 | 1303.92 | 6209.15 | 1303.92 | |
| Resorts | 25.47 | 10.93 | 491.36 | 105.40 | 516.83 | 116.33 | |
| Homestay | - | - | 795.12 | 149.09 | 795.12 | 149.09 | |
| Subtotal | 42.12 | 7109.10 | 7951.22 | 1709.62 (21.50 kg) | |||
| Ratio of type | Star-rated hotels 5.5%, General hotels 78%, Resorts 6.5%, Homestay 10% | ||||||
Note: CF = carbon footprint; The number in parentheses is the carbon footprint per capita.
Carbon footprint of international tourists’ accommodation in Chenzhou from 2014 to 2019.
| Year | Type of Accommodation | Accommodation Carbon Footprint (kg/kg) | Total Number of People (104) | Total CF (106 kg) | |||
|---|---|---|---|---|---|---|---|
| Number of People in a Room for One (104) | CF (106 kg) | Number of People in a Room for Two (104) | CF (106 kg) | ||||
| 2014 | Star-rated hotels | 20.0270 | 16.50 | 1.0541 | 0.43 | 21.0811 | 16.93 |
| General hotels | 3.9527 | 2.21 | - | - | 3.9527 | 2.21 | |
| Resorts | 1.3176 | 0.75 | - | - | 1.3176 | 0.75 | |
| Subtotal | 25.2973 | 1.0541 | 26.3514 | 19.89 (75.48 kg) | |||
| Ratio of type | Star-rated hotels 80%, General hotels 15%, Resorts 5% | ||||||
| 2015 | Star-rated hotels | 22.2556 | 18.34 | 1.7193 | 0.71 | 23.9749 | 19.05 |
| General hotels | 3.5518 | 1.99 | - | - | 3.5518 | 1.99 | |
| Resorts | 2.0719 | 1.19 | - | - | 2.0719 | 1.19 | |
| Subtotal | 29.2792 | 1.7193 | 29.5986 | 22.23 (75.10 kg) | |||
| Ratio of type | Star-rated hotels 81%, General hotels 12%, Resorts 7% | ||||||
| 2016 | Star-rated hotels | 23.2665 | 19.17 | 2.1513 | 0.89 | 25.4178 | 20.06 |
| General hotels | 3.0924 | 1.73 | - | - | 3.0924 | 1.73 | |
| Resorts | 2.1437 | 1.22 | - | - | 2.1437 | 1.22 | |
| Subtotal | 2877.26 | 2.1513 | 30.9239 | 23.01 (74.41 kg) | |||
| Ratio of type | Star-rated hotels 83%, General hotels 10%, Resorts 7%, | ||||||
| 2017 | Star-rated hotels | 27.8794 | 22.97 | 3.0093 | 1.24 | 30.8888 | 24.21 |
| General hotels | 3.2185 | 1.80 | - | - | 3.2185 | 1.80 | |
| Resorts | 2.8854 | 1.65 | - | - | 2.8854 | 1.65 | |
| Subtotal | 33.9833 | 3.0093 | 36.9926 | 27.66 (74.77 kg) | |||
| Ratio of type | Star-rated hotels 83.5%, General hotels 8.7%, Resorts 7.8% | ||||||
| 2018 | Star-rated hotels | 32.3899 | 26.69 | 5.4284 | 2.23 | 37.8183 | 28.93 |
| General hotels | 3.2339 | 1.81 | - | - | 3.2339 | 1.81 | |
| Resorts | 3.9627 | 2.27 | - | - | 3.8627 | 2.27 | |
| Subtotal | 39.4865 | 5.4284 | 44.9149 | 33.01 (73.50 kg) | |||
| Ratio of type | Star-rated hotels 84.2%, General hotels 7.2%, Resorts 8.6% | ||||||
| 2019 | Star-rated hotels | 40.5392 | 33.40 | 6.1576 | 2.54 | 46.6968 | 35.94 |
| General hotels | 3.5709 | 2.00 | - | - | 3.5709 | 2.00 | |
| Resorts | 4.6697 | 2.67 | - | - | 4.6697 | 2.67 | |
| Subtotal | 48.7798 | 6.1576 | 54.9374 | 40.61 (73.92 kg) | |||
| Ratio of type | Star-rated hotels 85%, General hotels 6.5%, Resorts 8.5% | ||||||
Note: CF = carbon footprint; The number in parentheses is the carbon footprint per capita.
Carbon footprint of tourism sightseeing activities in Chenzhou from 2014 to 2019.
| Year | Activity Type | Number of Participants (104) | Average Participant Frequency | Total Participant Frequency (104) | Emission Factor for CO2 (kg/(Person-per-Time)) | CF per Capita (kg/Person) | Total CF (106 kg) |
|---|---|---|---|---|---|---|---|
| 2014 | Forest Parks | 1088.18 | 1.2 | 1305.82 | 0.417 | 0.1630 | 5.45 |
| Theme |
3189.26 | 1.5 | 4783.89 | 0.417 | 0.5969 | 19.95 | |
| Nature Tours | 3342.96 | 1.6 | 5348.74 | 0.417 | 0.6671 | 22.30 | |
| Cultural |
1229.54 | 1.4 | 1581.36 | 1.72 | 0.8136 | 27.20 | |
| Total CF74.90 × 106 kg, CF per capita 2.2405 kg, DT CF74.22 × 106 kg, DT CF per capita 2.2378 kg, IT CF0.68 × 106 kg, IT CF per capita2.5802 kg | |||||||
| 2015 | Forest Parks | 1276.54 | 1.3 | 1659.50 | 0.417 | 0.1794 | 6.92 |
| Theme |
3624.51 | 1.5 | 5436.77 | 0.417 | 0.5876 | 22.67 | |
| Nature Tours | 3857.66 | 1.7 | 6558.02 | 0.417 | 0.7087 | 27.35 | |
| Cultural |
1397.85 | 1.4 | 1956.99 | 1.72 | 0.8725 | 33.66 | |
| Total CF 90.60 × 106 kg, CF per capita 2.3484 kg, DT CF89.81 × 106 kg, DT CF per capita 2.3340 kg, IT CF0.79 × 106 kg, IT CF per capita2.6690 kg | |||||||
| 2016 | Forest Parks | 1374.54 | 1.2 | 1649.45 | 0.417 | 0.1578 | 6.88 |
| Theme |
4027.54 | 1.6 | 6444.06 | 0.417 | 0.6162 | 26.87 | |
| Nature Tours | 4360.63 | 1.6 | 6977.01 | 0.417 | 0.6672 | 29.09 | |
| Cultural |
1511.85 | 1.4 | 2116.59 | 1.72 | 0.8351 | 36.41 | |
| Total CF99.25 × 106 kg, CF per capita 2.2760 kg, DT CF98.39 × 106 kg, DT CF per capita 2.2724 kg, IT CF0.86 × 106 kg, IT CF per capita2.7810 kg | |||||||
| 2017 | Forest Parks | 1593.21 | 1.2 | 1911.85 | 0.417 | 0.1293 | 7.97 |
| Theme |
5763.29 | 1.6 | 9221.26 | 0.417 | 0.6236 | 38.45 | |
| Nature Tours | 6141.47 | 1.7 | 10,440.45 | 0.417 | 0.7087 | 43.54 | |
| Cultural |
2475.68 | 1.3 | 3218.38 | 1.72 | 0.8981 | 55.36 | |
| Total CF145.32 × 106 kg, CF per capita 2.3574 kg, DT CF144.37 × 106 kg, DT CF per capita 2.3561 kg, IT CF0.95 × 106 kg, IT CF per capita2.5680 kg | |||||||
| 2018 | Forest Parks | 2135.44 | 1.2 | 2562.53 | 0.417 | 0.1484 | 10.69 |
| Theme |
6957.38 | 1.7 | 11,827.54 | 0.417 | 0.6844 | 49.32 | |
| Nature Tours | 7206.73 | 1.7 | 12,251.44 | 0.417 | 0.7090 | 51.09 | |
| Cultural |
3158.44 | 1.3 | 4105.97 | 1.72 | 0.9800 | 70.62 | |
| Total CF191.99 × 106 kg, CF per capita 2.6640 kg, DT CF178.74 × 106 kg, DT CF per capita 2.4957 kg, IT CF1.33 × 106 kg, IT CF per capita2.9500 kg | |||||||
| 2019 | Forest Parks | 4022.78 | 1.2 | 4827.34 | 0.417 | 0.2514 | 20.13 |
| Theme |
7424.59 | 1.6 | 11,879.34 | 0.417 | 0.6186 | 49.53 | |
| Nature Tours | 7851.77 | 1.7 | 13,348.01 | 0.417 | 0.6953 | 55.66 | |
| Cultural |
3462.09 | 1.3 | 4500.72 | 1.72 | 0.9669 | 77.41 | |
| Total CF202.73 × 106 kg, CF per capita 2.5322 kg, DT CF187.56 × 106 kg, DT CF per capita 2.3588 kg, IT CF1.52 × 106 kg, IT CF per capita2.7613 kg | |||||||
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint.
Carbon footprint of tourism entertainment activities in Chenzhou from 2014 to 2019.
| Year | Tourist Type | Activity Type | Number of Participants (104) | Average Participant Frequency | Emission Factor for CO2 * (kg/(Person-per-Time)) | CF per Capita (kg/Person) | Total CF (106 kg) |
|---|---|---|---|---|---|---|---|
| 2014 | DT | Cruise excursions | 2469.46 | 1 | 0.07 | 0.70 | 17.29 |
| Hot spring bathing | 379.81 | 1 | 2.24 | 2.24 | 8.51 | ||
| Yacht surfing | 2.21 | 1 | 15.3 | 15.3 | 0.34 | ||
| IT | Cruise excursions | 5.54 | 1 | 0.07 | 0.70 | 0.04 | |
| Hot spring bathing | 20.60. | 1 | 2.24 | 2.24 | 0.46 | ||
| Yacht surfing | 21.34 | 1 | 15.3 | 15.3 | 3.26 | ||
| Total CF29.90 × 106 kg, DT CF26.14 × 106 kg, IT CF 3.76 × 106 kg | |||||||
| 2015 | DT | Cruise excursions | 3075.43 | 1 | 0.07 | 0.70 | 21.52 |
| Hot spring bathing | 466.95 | 1. | 2.24 | 2.24 | 10.46 | ||
| Yacht surfing | 3.57 | 1 | 15.3 | 15.3 | 0.55 | ||
| IT | Cruise excursions | 6.72 | 1 | 0.07 | 0.07 | 0.05 | |
| Hot spring bathing | 26.93 | 1. | 2.24 | 2.24 | 0.60 | ||
| Yacht surfing | 22.24 | 1 | 15.3 | 15.3 | 3.40 | ||
| Total CF 36.58 × 106 kg, DT CF 32.53 × 106 kg, IT CF 4.05 × 106 kg | |||||||
| 2016 | DT | Cruise excursions | 36.5709 | 1 | 0.07 | 0.70 | 25.60 |
| Hot spring bathing | 473.04 | 1 | 2.24 | 2.24 | 10.60 | ||
| Yacht surfing | 4.25 | 1 | 15.3 | 15.3 | 0.65 | ||
| IT | Cruise excursions | 7.01 | 1 | 0.07 | 0.07 | 0.05 | |
| Hot spring bathing | 28.51 | 1 | 2.24 | 2.24 | 0.64 | ||
| Yacht surfing | 23.08 | 1 | 15.3 | 15.3 | 3.53 | ||
| Total CF 41.07 × 106 kg, DT CF 36.85 × 106 kg, IT CF 4.22 × 106 kg | |||||||
| 2017 | DT | Cruise excursions | 5034.28 | 1 | 0.07 | 0.70 | 35.23 |
| Hot spring bathing | 727.16 | 1 | 2.24 | 2.24 | 16.29 | ||
| Yacht surfing | 4.83 | 1 | 15.3 | 15.3 | 0.74 | ||
| IT | Cruise excursions | 9.34 | 1 | 0.07 | 0.07 | 0.06 | |
| Hot spring bathing | 34.37 | 1 | 2.24 | 2.24 | 0.77 | ||
| Yacht surfing | 26.19 | 1 | 15.3 | 15.3 | 4.85 | ||
| Total CF 57.94 × 106 kg, DT CF 52.26 × 106 kg, IT CF 5.68 × 106 kg | |||||||
| 2018 | DT | Cruise excursions | 6249.35 | 1 | 0.07 | 0.70 | 43.74 |
| Hot spring bathing | 1021.59 | 1 | 2.24 | 2.24 | 24.52 | ||
| Yacht surfing | 5.72 | 1 | 15.3 | 15.3 | 0.88 | ||
| IT | Cruise excursions | 10.24 | 1 | 0.07 | 0.07 | 0.07 | |
| Hot spring bathing | 41.99 | 1 | 2.24 | 2.24 | 0.94 | ||
| Yacht surfing | 33.17 | 1 | 15.3 | 15.3 | 5.08 | ||
| Total CF 75.23 × 106 kg, DT CF 69.14 × 106 kg, IT CF 6.09 × 106 kg | |||||||
| 2019 | DT | Cruise excursions | 75.3329 | 1 | 0.07 | 0.70 | 52.73 |
| Hot spring bathing | 1366.36. | 1 | 2.24 | 2.24 | 30.60 | ||
| Yacht surfing | 6.43 | 1 | 15.3 | 15.3 | 0.98 | ||
| IT | Cruise excursions | 12.46 | 1 | 0.07 | 0.70 | 0.09 | |
| Hot spring bathing | 51.97 | 1 | 2.24 | 2.24 | 1.16 | ||
| Yacht surfing | 39.59 | 1 | 15.3 | 15.3 | 6.06 | ||
| Total CF 91.62 × 106 kg, DT CF 84.31 × 106 kg, IT CF 7.31 × 106 kg | |||||||
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint; * the CO2 emission factor of the cruise excursion is (0.07 kg/(person-km)), and the average distance per trip is 10 km.
Carbon footprint of Tourism Shopping in Chenzhou from 2014 to 2019 *.
| Year | Fruits | Chestnuts | Dry Fish | Total CF (106 kg) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Weight (104 kg) | Carbon Emission Factor (kg/kg) | CF (106 kg) | Weight (104 kg) | Carbon Emission Factor (kg/kg) | CF (106 kg) | Weight (104 kg) | Carbon Emission Factor (kg/kg) | CF (106 kg) | ||
| 2014 | 2754.3 | 0.15 | 4.13 | 11.5 | 0.15 | 0.02 | 1.6 | 0.72 | 0.01 | 4.17 |
| 2015 | 3127.6 | 0.15 | 4.69 | 12.8 | 0.15 | 0.02 | 1.8 | 0.72 | 0.01 | 4.72 |
| 2016 | 3573.5 | 0.15 | 5.36 | 15.9 | 0.15 | 0.02 | 2.4 | 0.72 | 0.02 | 5.40 |
| 2017 | 5891.4 | 0.15 | 8.84 | 21.7 | 0.15 | 0.03 | 3.5 | 0.72 | 0.03 | 8.89 |
| 2018 | 6275.7 | 0.15 | 9.41 | 25.5 | 0.15 | 0.04 | 3.7 | 0.72 | 0.03 | 9.48 |
| 2019 | 7038.4 | 0.15 | 10.57 | 30.2 | 0.15 | 0.05 | 4.1 | 0.72 | 0.04 | 10.66 |
* The carbon emission factors in this table are cited from literature [
Carbon footprint of tourism waste disposal in Chenzhou from 2014 to 2019.
| Year | Tourist Type | Landfill | Release of CH4 from Landfills | Total CF (106 kg) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Waste Volume (104 kg) | CF (106 kg) | Waste Volume (104 kg) | Percentage of Degradable Organic Carbon | Decomposition Rate of Degraded Organic Carbon | CH4 of C in Ratio | Greenhouse Effect Value of CH4 Relative to CO2 | Correction Factor | CF (106 kg) | |||
| 2014 | DT | 5757.31 | 2.16 | 5757.31 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 66.33 | 68.49 |
| IT | 54.69 | 0.02 | 54.69 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 0.63 | 0.65 | |
| Total | 5812.00 | 2.18 | 5812.00 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 66.96 | 69.14 | |
| 2015 | DT | 5968.40 | 2.24 | 5968.40 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 68.76 | 71.00 |
| IT | 61.35 | 0.02 | 61.35 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 0.71 | 0.73 | |
| Total | 6029.75 | 2.26 | 6029.75 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 69.47 | 71.73 | |
| 2016 | DT | 6732.68 | 2.53 | 6732.68 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 77.56 | 80.09 |
| IT | 63.55 | 0.03 | 63.55 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 0.73 | 0.76 | |
| Total | 6796.23 | 2.56 | 6796.23 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 78.29 | 80.85 | |
| 2017 | DT | 11,397.15 | 4.73 | 11,397.15 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 131.30 | 136.03 |
| IT | 86.56 | 0.03 | 86.56 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 1.00 | 1.03 | |
| Total | 11,483.71 | 4.76 | 11,483.71 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 132.30 | 137.06 | |
| 2018 | DT | 13,177.71 | 4.94 | 13,177.71 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 151.80 | 156.74 |
| IT | 103.75 | 0.04 | 103.75 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 1.19 | 1.23 | |
| Total | 13,281.46 | 4.98 | 13,281.46 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 152.99 | 157.97 | |
| 2019 | DT | 14,550.88 | 2.46 | 14,550.88 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 167.62 | 170.08 |
| IT | 127.73 | 0.05 | 127.73 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 1.47 | 1.52 | |
| Total | 14,678.61 | 2.51 | 14,678.61 | 0.19 | 0.77 | 0.75 | 21 | 0.5 | 169.09 | 171.60 | |
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint.
Total carbon footprint of tourism in Chenzhou from 2014 to 2019.
| Year | Tourist Type | Carbon Footprint of Tourism by Subsector (106 kg) | Total CF (106 kg) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Transportation | Catering | Accommodation | Sightseeing | Entertainment | Shopping | Waste Disposal | |||
| 2014 | DT | 7143.52 | 481.57 | 711.06 | 74.22 | 26.14 | 4.17 | 68.49 | 8509.17 |
| IT | 545.07 | 6.06 | 19.89 | 0.68 | 3.76 | - | 0.65 | 576.11 | |
| Total | 7688.09 | 487.63 | 730.95 | 74.90 | 29.90 | 4.17 | 69.14 | 9085.28 | |
| 2015 | DT | 8587.99 | 555.85 | 821.68 | 89.81 | 32.53 | 4.72 | 71.00 | 10,163.58 |
| IT | 631.49 | 6.80 | 22.23 | 0.79 | 4.05 | - | 0.73 | 666.09 | |
| Total | 9219.48 | 562.65 | 843.91 | 90.60 | 36.58 | 4.72 | 71.73 | 10,829.67 | |
| 2016 | DT | 9763.84 | 628.27 | 939.93 | 98.39 | 36.85 | 5.40 | 80.09 | 11,613.79 |
| IT | 666.81 | 7.11 | 23.01 | 0.86 | 4.22 | - | 0.76 | 702.77 | |
| Total | 10,430.65 | 635.38 | 962.49 | 99.25 | 41.07 | 5.40 | 80.85 | 12,255.54 | |
| 2017 | DT | 12,995.82 | 1007.36 | 1342.67 | 144.37 | 52.26 | 8.89 | 136.03 | 15,687.40 |
| IT | 781.42 | 9.91 | 27.66 | 0.95 | 5.68 | - | 1.03 | 826.65 | |
| Total | 13,777.24 | 1017.27 | 1370.33 | 145.32 | 57.94 | 8.89 | 137.06 | 16,414.05 | |
| 2018 | DT | 16,727.51 | 1177.40 | 1548.81 | 1.9166 | 69.14 | 9.48 | 156.74 | 19,879.75 |
| IT | 965.57 | 12.04 | 33.01 | 1.32 | 6.09 | - | 1.23 | 1019.26 | |
| Total | 17,693.08 | 1189.44 | 1581.82 | 192.98 | 75.23 | 9.48 | 157.97 | 20,899.01 | |
| 2019 | DT | 18,288.71 | 1307.18 | 1709.62 | 20.121 | 84.31 | 10.66 | 170.08 | 21,771.77 |
| IT | 1171.96 | 14.73 | 40.61 | 1.52 | 7.31 | - | 1.52 | 1237.65 | |
| Total | 19,460.67 | 1321.91 | 1750.23 | 202.73 | 91.62 | 10.66 | 171.60 | 23,009.42 | |
Note: DT = domestic tourists; IT = international tourists; CF = carbon footprint.
References
1. Lenzen, M.; Sun, Y.Y.; Faturay, F.; Ting, Y.P.; Geschke, A.; Malik, A. The carbon footprint of global tourism. Nat. Clim. Chang.; 2018; 8, pp. 522-528. [DOI: https://dx.doi.org/10.1038/s41558-018-0141-x]
2. Dogan, E.; Ozturk, I. The influence of renewable and non-renewable energy consumption and real income on CO2 emissions in the USA: Evidence from structural break tests. Environ. Sci. Pollut. Res.; 2017; 24, pp. 10846-10854. [DOI: https://dx.doi.org/10.1007/s11356-017-8786-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28293824]
3. Sharif, A.; Raza, S.A.; Ozturk, I.; Afshan, S. The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations. Renew Energy; 2019; 133, pp. 685-691. [DOI: https://dx.doi.org/10.1016/j.renene.2018.10.052]
4. Khan, S.A.R.; Yu, Z.; Sharif, A.; Golpîra, H. Determinants of economic growth and environmental sustainability in South Asian Association for Regional Cooperation: Evidence from panel ARDL. Environ. Sci. Pollut. Res.; 2020; 27, pp. 45675-45687. [DOI: https://dx.doi.org/10.1007/s11356-020-10410-1]
5. Nathaniel, S.; Khan, S.A.R. The nexus between urbanization, renewable energy, trade, and ecological footprint in ASEAN countries. J. Clean. Prod.; 2020; 272, 122709. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122709]
6. Sharif, A.; Baris-Tuzemen, O.; Uzuner, G.; Ozturk, I.; Sinha, A. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from quantile ARDL approach. Sustain. Cities Soc.; 2020; 57, 102138. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102138]
7. Hong, Z.; Wang, L.; Zhang, C. Influencing factors of regional tourism eco-efficiency under the background of green development in the western China. Acta Ecol. Sin.; 2021; 41, pp. 3512-3524.
8. Sun, Y.Y. A framework to account for the tourism carbon footprint at island destinations. Tour. Manag.; 2014; 45, pp. 16-27. [DOI: https://dx.doi.org/10.1016/j.tourman.2014.03.015]
9. Yuan, Y.J.; Jiang, Y.M. Calculation of carbon emissions of Shandong tourism by input-output method. J. Cent. South Univ. For. Technol. Soc. Sci.; 2013; 7, pp. 1–5, 8. [DOI: https://dx.doi.org/10.14067/j.cnki.1673-9272.2013.03.004]
10. Dong, H.Y.; Liu, Q.P.; Zhou, L.; Yu, Z.P. Dynamics Comparison of Tourism Carbon Footprint with Tourism Carbon Capacity in Jiangsu Province. Ecol. Econ.; 2018; 34, pp. 183-187.
11. Yuan, H.; Nie, K.X.; Xu, X.Y. Relationship between tourism number and air quality by carbon footprint measurement: A case study of Jiuzhaigou Scenic Area. Environ. Sci. Pollut. Res.; 2021; 28, pp. 20894-20902. [DOI: https://dx.doi.org/10.1007/s11356-020-12068-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33405174]
12. Zhong, Y.D.; Shi, S.Y.; Li, S.H.; Luo, F.; Luo, W.L. Construction and empirical study of carbon emission measurement framework of Chinese tourism industry. China Popul. Resour. Environ.; 2014; 2014, pp. 78-86.
13. Cadarso Vecina, M.Á.; Gómez Sanz, N.; López Santiago, L.A.; Tobarra Gómez, M.Á.; Zafrilla, J. Tourism environmental responsibility: The ignored role of investment. Proceedings of the ETSG Thirteenth Annual Conference; Copenhagen, Danmark, 10 September 2011.
14. Munday, M.; Tune, R.K.; Jones, C. Accounting for the carbon associated with regional tourism consumption. Tour. Man-Agement; 2013; 36, pp. 35-44. [DOI: https://dx.doi.org/10.1016/j.tourman.2012.11.005]
15. Zhang, Z.J. Tourism carbon footprint calculation and low carbon tourism development in Liaoning Coastal Economic Belt under the new norm. Ecol. Econ.; 2016; 32, pp. 144-147.
16. Ma, J. Toursim carbon footprint measurement in Gansu Province based on the end consumption. J. Hebei Tour. Vocat. Coll.; 2020; 25, pp. 31-34.
17. Shang, Q. Study on carbon footprint in Jigongshan Scenic Spot in Henan Province. J. Fo Zhejiang For. Sci. Technol.; 2019; 39, pp. 76-80.
18. Cui, Y.; Zhang, Y.; He, D.X.; Tian, Q. Calculation and evaluation of tourism carbon footprint in Rizhao based on final consumption. J. Green Sci. Technol.; 2022; 24, pp. 154-158.
19. Huang, Y.F.; Zhao, J. Analysis and evaluation on carbon footprint of visitors in Lijiang tourism industry. China For. Econ.; 2012; 1, pp. 13-16. [DOI: https://dx.doi.org/10.13691/j.cnki.cn23-1539/f.2012.01.007]
20. Xie, Y.F.; Zhao, Y. Measuring carbon dioxide emissions from energy consumption by tourism in Yangtze River Delta. Geogr. Res.; 2012; 31, pp. 429-438+5.
21. Jiang, D.H.; Ji, X. Tourism carbon footprint on final consumption in Shandong Province. China Popul. Resour. Environ.; 2015; 25, (Suppl. S1), pp. 450-453.
22. Zheng, Q.M.; Chen, Z.Q. A study on eco-efficiency of tourism based on carbon footprint—Jiujiang city, Jiangxi Province as an example. J. Nat. Sci. Hunan Norm. Univ.; 2022; 45, pp. 74-82.
23. Hannah, S.; Josefine, G.; Jukka, H. Carbon footprint of inbound tourism to Iceland: A consumption-based life-cycle assessment including direct and indirect emissions. Sustainability; 2016; 8, 1147.
24. Koiwanit, J.; Filimonau, V. Carbon footprint assessment of home-stays in Thailand. Resourc. Conserv. Recycl.; 2021; 105123, 164. [DOI: https://dx.doi.org/10.1016/j.resconrec.2020.105123]
25. Becken, S. The Carbon Footprint of Domestic Tourism. Available online: https://researcharchive.lincoln.ac.nz/bitstream/handle/10182/1216/becken_carbon_footprint.pdf?sequence=1&isAllowed=y (accessed on 22 May 2022).
26. Gössling, S.; Hall, M. Swedish tourism and climate change mitigation: An emerging conflict. Scand. J. Hosp. Tour.; 2008; 8, pp. 141-158. [DOI: https://dx.doi.org/10.1080/15022250802079882]
27. Gössling, S. Global environmental consequences of tourism. Glob. Environ. Chang.; 2002; 12, pp. 283-302. [DOI: https://dx.doi.org/10.1016/S0959-3780(02)00044-4]
28. Ma, Y.; Liu, J. The core value of tourism eco-efficiency in the context of green development and strategies to enhance it. J. Tour.; 2016; 31, pp. 1-3.
29. Tan, Q.C. Greenhouse gas emissions from agriculture in China: Status and challenges. China Popul. Resour. Environ.; 2011; 21, pp. 69-75.
30. Yao, Z.G.; Chen, T.; Yin, S.B.; Li, X.G. An empirical analysis of regional tourism eco- efficiency-an investigation of Hainan Province as an example. Geoscience; 2016; 36, pp. 417-423.
31. Gössling, S.; Hansson, C.B.; Hörstmeier, O.; Saggel, S. Ecological footprint analysis as a tool to assess tourism sustainability. Ecol. Econ.; 2002; 43, pp. 199-211. [DOI: https://dx.doi.org/10.1016/S0921-8009(02)00211-2]
32. Becken, S.; Patterson, M. Measuring national carbon dioxide emissions from tourism as a key step towards achieving sustainable tourism. J. Sustain. Tour.; 2006; 14, pp. 323-338. [DOI: https://dx.doi.org/10.2167/jost547.0]
33. Zhen, Y. Tourism eco-efficiency assessment-a comparative study based on ecological footprint and carbon footprint. For. Econ. Issues; 2014; 34, pp. 474-480.
34. Chen, B.H.; Zhang, L.; Hu, Y. Characterization and prediction of domestic waste generation in scenic areas. Environ. Sanit.; 2011; 19, pp. 21–23, 27.
35. Liu, Z.G. Corbon stock and greenhouse gasses emission of wetland ecosystem. Sci. Geogr. Sin.; 2004; 24, pp. 634-639.
36. Lu, H. Research on the carbon footprint of tourism under the threshold of ecological civilization. Econ. Vis.; 2017; 2, pp. 1-6.
37. Huang, T.Y.; Tang, Z. Estimation of tourism carbon footprint and carbon capacity. Int. J. Low-Carbon Technol.; 2021; 16, pp. 1040-1046. [DOI: https://dx.doi.org/10.1093/ijlct/ctab026]
38. Zhang, Z. Research on Eco-Efficiency of Tourism in Wuhan Based on Carbon Footprint. Master’s Thesis; Huazhong Normal University: Wuhan, China, 2017.
39. Zhai, C.Y.; Gong, C. Research and application of carbon footprint: A literature review. Hainan Financ.; 2022; 5, pp. 39-50.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.