Accounting for tourism carbon emissions: A consumption stripping perspective based on the tourism satellite account

Abstract

Limitations in statistical data and differences in accounting methods have hindered the accuracy of tourism carbon emissions accounting. In this research, based on the Tourism Satellite Account (TSA) and underpinned by the logic of “accounting basis–key coefficient–accounting objective,” a comprehensive decomposition accounting method is built from a consumption stripping perspective. First, it classifies the tourism industry by the “sector–industry–product” structure into seven sectors, 13 industries, and 22 characteristic products/services. Next, it strips the actual tourism consumption from the tourism industry by constructing two key coefficients. Finally, it transforms tourism consumption data into carbon emissions data by introducing tourism ecological efficiency. Taking Guangdong province of China as an example, its tourism carbon emissions are calculated from 2010 to 2020 using the proposed method. The results reveal the distribution structure of tourism carbon emissions and confirm the scientific and accurate nature of this accounting method.

Keywords

tourism carbon emissions tourism satellite account (TSA)consumption stripping accounting method tourism ecological efficiency

Introduction

As a core issue in climate change, carbon emissions have gained widespread attention and have come to embody a global environmental concern (Paramati et al., 2017). Recent years have witnessed soaring carbon emissions: according to the International Energy Agency’s latest Report on Global Carbon Emissions, these emissions reached 33 billion tons in 2019. Carbon emissions specifically appear to be exacerbating rising temperatures and environmental changes. If the world attempts to realize the climate goal of “controlling the temperature rise within 2°C” as established under the Paris Agreement, the next 10 years should be devoted to reducing emissions by 2.7% annually (United Nations Environment Programme [UNEP], 2019). More active and efficient energy-saving and emissions-reducing measures should therefore be taken in all sectors (Zha et al., 2021).

The tourism industry is characterized by low energy consumption but high output, hence its being labeled a “low-carbon industry.” (Lee et al., 2013). Nevertheless, “low-carbon” is not analogous to “zero-carbon.” Rapid tourism development suggests that the number of international tourists will exceed 1.4 billion per year before the COVID-19 pandemic; such traffic is projected to amplify energy consumption and carbon emissions in this industry, drawing more attention to potential environmental problems (Sun et al., 2019, 2020; Sun,2014). The United Nations World Tourism Organization (UNWTO) further estimates that, by the year 2030, carbon emissions due to tourism transportation alone will reach 1.998 billion tons, accounting for 5.3% of man-made CO₂ emissions (UNWTO, 2008). According to the latest research, the proportion has reached 8% (Lenzen et al., 2018). Tourism has therefore gradually become a key contributor to global carbon emissions; tourism carbon emissions have also aroused great concern among scholars (Tang et al., 2017). Thus, energy conservation and emissions reduction now stand at the top of the industry agenda.

The main three-phase logic line of carbon emissions reduction, “measurement–emissions reduction–compensation,” holds carbon accounting as paramount: this task is the prerequisite to clarifying emissions reduction goals and devising energy-saving and emissions-reducing policies. It is accordingly essential to determine the actual reduction in carbon emissions in the tourism industry. However, tourism lacks distinct industry boundaries; it is closely linked with other industries both directly and indirectly (Tarancón and del Río, 2007; Zha et al., 2019). This circumstance has caused many industries to contribute to tourism-related carbon emissions. The tourism industry also has yet to be addressed as a specific industry by national statistical standards. Authoritative statistics are therefore lacking; this issue further impedes accurate accounting. Additionally, no globally acknowledged carbon accounting method exists for tourism carbon emissions: a unified set of standards is unavailable to pinpoint the connotations, system boundaries, accounting criteria, and research methods for carbon accounting (Meng et al., 2017; Tang et al., 2017; Zha et al., 2021). These myriad factors have spawned diverse research methods and findings concerning carbon emissions accounting. Inaccurate accounting results have affected the development and initiation of energy-saving and emission-reducing policies along with the formulation and implementation of policies around tourism-related carbon compensation and carbon finance. Imprecise accounting can potentially influence regional economic development as well. As such, establishing a more scientific accounting method for tourism carbon emissions represents a critical but complex research aim.

Based on the preceding discussion, this research initially focuses on the concept of the Tourism Satellite Account (TSA) to realize a scientific division of the tourism industry. The study then shifts to tourism consumption: the tourism proportion and tourism stripping coefficient are combined, and tourism ecological efficiency is considered to develop a more scientific accounting method to calculate tourism carbon emissions. The proposed system lends a fresh perspective to tourism carbon emissions accounting research while compensating for existing knowledge gaps. In doing so, this effort enhances the theoretical system for carbon accounting in low-carbon tourism research and provides novel quantitative findings regarding the tourism industry’s environmental externality. The research scope and content of tourism ecological management are expanded in kind. The proposed method also returns more accurate carbon emissions accounting results to help relevant departments in several respects: verifying the reduction potential of tourism carbon emissions, identifying emissions reduction goals, and formulating efficient emissions reduction measures based on a scientific foundation.

Literature review

Two accounting approaches for tourism carbon emissions accounting

The swift development of tourism has caused industry-related energy and environmental issues to elicit considerable discussion in academic circles-especially tourism carbon emissions, which have received increasing attention with respect to global warming. Measuring tourism carbon emissions continues to pose a great challenge (Tang and Ge, 2018; Zha et al., 2020). The tourism industry is a rich domain that satisfies numerous tourist needs. This industry integrates products and services related to catering (i.e., food- and beverage-serving services), accommodation (i.e., accommodation services for visitors), transportation (i.e., passenger transport services, including railway, road, water, air, etc.), sightseeing (including tourist attraction service and travel agencies and other reservation services), shopping (i.e., tourism shopping services), and entertainment sectors (including cultural services, sports and recreational services) throughout the travel process (Zha et al., 2019). Tourism involves extensive coverage, diverse associated industries, and an array of complex products and services. Tourism carbon emissions refer to greenhouse gas emissions generated by tourists’ consumption of tourism products, indicated by the mass of CO₂. This tourism emissions metric is a key means of assessing the tourism industry’s eco-environmental impacts and serves as an important criterion to measure the degree of sustainable development (Jones and Munday, 2007; Luo et al., 2020). Since Gössling (2000) initially proposed a method for estimating the tourism industry’s carbon emissions, accounting for this industry’s carbon emissions accounting has garnered close attention. The Climate Change and Tourism – Responding to Global Challenge report, released through a collaboration among UNWTO, UNEP, and other international organizations (UNWTO et al., 2008), further enriched research in this field. This report also led to the formation of two prevailing accounting approaches, namely, “top-down” and “bottom-up” (see Table 1).

Table 1.

Comparative analysis of two accounting approaches.

Category	Top-down approach	Bottom-up approach
Definition	The top-down approach is based on the energy consumption data/carbon emissions data of a specific country or region; it calculates the total percentage of the tourism industry’s carbon emissions to realize tourism industry accounting.	The bottom-up approach is based on tourists’ energy consumption data; it calculates energy consumption and carbon emissions in ascending order (level by level) to determine total carbon emissions.
Characteristics	The top-down approach refers to statistical data from government departments and estimates carbon emissions from the national level to the local level.	Referring to the relevant energy consumption coefficient, the bottom-up approach calculates the total carbon emissions based on tourist statistics level by level and item by item.
Advantages	The top-down approach can differentiate the tourism industry from other industries. Accounting results can then be compared with those of other sectors.	The bottom-up approach has a simple logic algorithm; there is no need to consider indirect energy consumption and emissions.
Disadvantages	The top-down approach has high standards for industrial and energy consumption statistics at the national and regional levels; data availability is accordingly limited. It is difficult to use this method to achieve accounting goals for most nations/regions.	In addition to relying on comprehensive tourism statistics, the bottom-up approach requires data from large-scale tourist surveys. Differentiating the tourism industry from other industries is therefore difficult, leading to significant deviations in accounting results.

Source: Developed by the authors.

The “top-down” approach calculates the percentage of the tourism industry’s carbon emissions in the energy consumption data or carbon emissions data of a certain country or region, thus determining tourism industry carbon emissions (Meng et al., 2016; Munday et al., 2013). For example, Patterson et al. (2004) assembled a tourism department economic environmental input and output table to calculate tourism-related carbon emissions and evaluate the environmental impact of New Zealand’s tourism industry, thereby re-identifying the industrial position of the tourism industry. On the contrary, the “bottom-up” approach is based on the energy consumption data of tourists in different tourism sectors. By calculating energy consumption and carbon emissions in an ascending manner level by level, this approach can determine total tourism carbon emissions (Rico et al., 2019; Shah et al., 2019; Tsai et al., 2014). In one case, Becken et al. (2003) considered the energy consumption and carbon emissions of tourism-related transportation, accommodation, and activities to obtain the “Energy Bill” for New Zealand’s tourism industry. A comparative analysis of these two methods reveals that the “top-down” approach includes high standards for the statistical monitoring of industrial economic data and carbon emissions at the national or regional level. Specifically, the “top-down” approach requires the establishment of the System of Integrated Environmental and Economic Accounting (SEEA) and the TSA. This approach has been applied in many countries (e.g., New Zealand, Switzerland, and Australia) that have established a complete SEEA and TSA. As research progressed, other scholars have further used input–output models to study direct and indirect tourism-related carbon emissions (Dwyer et al., 2010; Sun, 2016).The “top-down” approach can be more challenging to implement in countries or regions without these systems (Becken and Patterson, 2006; Dwyer et al., 2010; Nielsen et al., 2010; Patterson et al., 2004). By contrast, the “bottom-up” approach is grounded in tourism energy consumption. By relying on tourism statistics and data on tourists’ energy consumption, and by combining coefficients or parameters related to tourism energy consumption, the “bottom-up” approach allows for carbon emissions accounting in tourism sectors such as transportation, accommodation, and sightseeing. This approach also does not impose substantial data demands, hence why researchers tend to adopt it (Cadarso et al., 2015; Howitt et al., 2010). However, in addition to relying on comprehensive tourism statistics, the bottom-up approach requires data from large-scale tourist surveys. Differentiating the tourism industry from other industries is therefore difficult, leading to significant deviations in accounting results.

Major problems of the two accounting approaches

Though a relatively feasible accounting method can be established based on these two popular perspectives, tourism carbon emissions accounting can also occur at the national or even regional level (Tang and Ge, 2018). Further comparative analysis has also shown that problems persist in the literature which can affect the precision of both accounting steps and results. First, the specificity of tourism industrial division can fundamentally influence accounting accuracy. The tourism industry lacks clear industrial boundaries; tourism-related sectors are mainly defined by industrial classification standards of the System of National Accounts (SNAs). The industry’s accounting scope concentrates on three sectors—transportation, accommodation, and sightseeing—while ignoring the respective impacts of catering, entertainment, and shopping on tourism carbon emissions. Useful data may therefore be omitted during the accounting process. Second, including (or excluding) the tourism stripping coefficient as a primary accounting indicator can lead to significant deviations in accounting results. The tourism stripping coefficient refers to the percentage of tourists' actual consumption in the total output of the tourism sector (industry). Accounting indicators related to carbon emissions, such as total tourism revenue and the total number of tourists, are most often adopted. These metrics reflect the overall quantity of a given category of expected industry output. Non-tourism industries are therefore not stripped, which can cause overestimation or repeated estimation of accounting results. TSA can effectively bridge these two gaps in defining the boundaries of the tourism industry and accounting the tourist-induced consumption from both the tourism consumption side and the tourism supply side. Third, selection differences in the carbon emissions correlation coefficient and parameters can generate discrepant accounting results. Many scholars have referred to tourism energy consumption in their accounting processes (Becken and Patterson, 2006; Demeter et al., 2022; Gössling, 2000; Qiu et al., 2017). Their selection of tourism carbon emissions varies; examples include the annual occupancy rate of beds related to accommodation, average energy consumption/carbon emissions per bed, proportion of tourism-related transportation, unit energy consumption/carbon emissions of transportation modes, and proportion and per capita energy consumption/carbon emissions of sightseeing-related tourism activities. A unified set of criteria for correlation coefficients and parameters does not yet exist. This lack of standardization further explains inconsistent accounting results in the industry.

Methodologies

Several questions must be answered to increase accounting results’ precision and accuracy. First, how should the tourism industry be scientifically divided? Second, how should relevant sectors be stripped from tourism-related consumption? Third, how should stripped tourism consumption data be converted into carbon emissions data, and how should total carbon emissions be decomposed to identify the specific emissions distribution structure? The TSA can provide an efficient means of addressing these issues. This section builds a comprehensive decomposition accounting method for tourism carbon emissions accounting based on TSA.

Accounting analytical framework based on TSA

The TSA is a satellite account independent of the account of the National Economic Accounting Center. Taking the basic concept and statistical principle of the SNA as its foundation, the TSA represents a new, authoritative, efficient method and international standard that can be used to measure the tourism industry’s economic impacts. The TSA has gained broad recognition from relevant international organizations and governments in multiple countries; it has hence become a scientific tool for government departments to draw up tourism-related policies. Tourism consumption is one of the key concepts and indicators in TSA; actually, besides the amount paid for the acquisition of consumption goods and services, as well as valuables for own use or to give away, for and during tourism trips, which corresponds to monetary transactions, tourism consumption also includes services associated with vacation accommodation on own account, tourism social transfers in kind and other imputed consumption. These transactions need to be estimated using sources different from information collected directly from the visitors. Under the accounting premise of the TSA, the output generated by tourism consumption is separated from tourism-related industries and departments to measure the economic contributions of tourism respectively based on demand and supply. Specifically, after defining and classifying tourism sectors and associated products, the TSA returns 10 accounts and tables whose content is connected through data statistics and large-scale sampling. These accounts and tables can then be applied to core indicator accounting (e.g., total regional tourism consumption, direct added increase of the tourism industry, and total tourism-related employment). The document Tourism Satellite Account: Recommended Methodological Framework 2008 (TSA:RMF, 2008) holds the latest international standards (United Nations Statistics Division et al., 2008). In addition to defining the TSA-related conceptual system, TSA:RMF 2008 identifies the main tabled statistical information for 10 accounts (see Table 2). Non-currency indicators include the following: the number of trips and overnight accommodations, classified by tourism forms and tourist types; the number of inbound visits and overnight accommodations, classified by transportation modes; the number of grassroots unit (i.e., individual tourism operators/firms) quantity and bearing capacity, classified by accommodation types; and the number of grassroots units of the tourism industry divided by the average number of positions.

Table 2.

TSA-related table content.

Table No.	Content
Table 1	Inbound tourism expenditure by products and visitor classes
Table 2	Domestic tourism expenditure by products, visitor classes, and trip types
Table 3	Outbound tourism expenditure by products and visitor classes
Table 4	Internal tourism consumption by products
Table 5	Production accounts of tourism industries and other industries (at basic prices)
Table 6	Total domestic supply and internal tourism consumption (at purchasers’ prices)
Table 7	Employment in tourism industries
Table 8	Tourism-related gross fixed capital formation of tourism industries and other industries
Table 9	Tourism collective consumption by products and levels of government
Table 10	Non-monetary indicators

Source: Developed by the authors.

Tables 1, 2, 4, 5 and 6 are most frequently cited in the extant literature. Figure 1 depicts their relationship. The relationship between tourism consumption and tourism supply enables the accounting of core indices such as total tourism consumption and total tourism employment.

Table 3.

Division of tourism industry.

Tourism sector	Tourism industries	Tourism characteristic products (services)
Accommodation	1. Accommodation industry	1. Accommodation services for visitors
		1.1 hospitality and other accommodation services
		1.2 accommodation services associated with all types of vacation home ownership
Catering	2. Catering industry	2. Food and beverage services
Transportation	3. Railway passenger transport industry	3. Railway passenger transport services
	4. Road passenger transport industry	4. Road passenger transport services
		4.1 highway passenger transport services
		4.2 Urban public transport services
	5. Water passenger transport industry	5. Water passenger transport services
	6. Air passenger transport industry	6. Air passenger transport services
	7. Transport equipment rental industry	7. Transport equipment rental services
Sightseeing	8. Travel agencies and other reservation services	8. Travel agencies and other reservation services
		8.1 travel agency services
		8.2 Natural ecological protection and scenic area management services
Entertainment	9. Culture and recreation industry	9. Cultural and art services
		9.1 Artistic performances
		9.2 Museum and other cultural services
	10. Sports and recreation industry	10. Sports and recreational services
	11. Local tourism entertainment industry	11. Local tourism entertainment services
Shopping	12. Retail trade industry	12. Tourism shopping services
Others	13. Other tourism services	13. Other tourism services
		13.1 Banking services
		13.2 Insurance services
		13.3 Postal services, telecommunications, and other information transmission services
		13.4 Neighborhood services
		13.5 tourism-related services with local characteristics

Source: Developed by the authors.

Table 4.

Overview of GDTSA tables.

No.	Content	Data sources	Key indicators
Table 1	Inbound tourist expenditure in Guangdong	Guangdong tourism yearbook 2011;	Travel expenditure ratio
		Guangdong international tourist survey 2010;
		Guangdong domestic tourist survey 2010
Table 2	Tourist expenditure by Guangdong residents within Guangdong province	Guangdong domestic tourist survey 2010;	Travel expenditure ratio of Guangdong residents (within Guangdong province)
Table 2		Guangdong tourism yearbook 2011
Table 3	Tourist expenditure by Guangdong residents outside Guangdong but within China	Guangdong domestic tourist survey 2010;	Travel expenditure ratio of Guangdong residents (outside Guangdong but within China)
Table 3		Guangdong tourism yearbook 2011
Table 4	Total tourism consumption by products	Tables 1 and 2;	Total tourism consumption
		Guangdong domestic tourist survey 2010;
		Tourism and tourism-related enterprises surveys by products 2010
Table 5	Production account of tourism industries and other industries	Guangdong statistical yearbook 2011;	Travel output ratio and total tourism output
		Guangdong input–output table 2007;
		Guangdong input–output extension table 2010;
		Guangdong economic survey yearbook 2008;
		Tourism and tourism-related enterprises surveys by products 2010
Table 6	Total tourism supply and tourism consumption ratios	Tables 4 and 5;	Tourism stripping coefficient
Table 6	Total tourism supply and tourism consumption ratios	Tourism and tourism-related enterprises surveys by products 2010	Tourism stripping coefficient
Table 7	Tourism employment	Guangdong statistical yearbook 2011;	Total tourism employment
		Guangdong economic survey yearbook 2008;
		Tourism and tourism-related enterprises surveys by products 2010
Table 8	Gross tourism fixed capital formation	Guangdong statistical yearbook 2011;	Tourism fixed Investment
Table 8	Gross tourism fixed capital formation	Guangdong convention survey report 2010	Tourism fixed Investment
Table 9	Non-monetary indicators of tourism in Guangdong	1. Tables 3, 4, and 5;	Number of tourists and Number of legal entities in the tourism industry
Table 9	Non-monetary indicators of tourism in Guangdong	2. Guangdong statistical yearbook 2011

Source: Developed by the authors.

Table 5.

Guangdong province’s tourism proportion and tourism stripping coefficient by sector in 2010.

Sector	Tourism consumption (100 million yuan)	Tourism proportion (%)	Total tourism supply (100 million yuan)	Stripping coefficient
Accommodation	703.12	18.36	1747.31	0.4024
Catering	1180.95	30.84	2049.33	0.5763
Transportation	1048.14	27.38	2199.63	0.4765
Sightseeing	88.04	2.30	89.94	0.9789
Entertainment	274.61	7.17	309.87	0.8862
Shopping	367.15	9.59	3442.59	0.1067
Others	166.77	4.36	6337.78	0.0263
Total	3828.78	100	16,176.45	—

Source: Developed by the authors.

Figure 1.

Relationship of TSA Tables 1, 2, 4, 5 and 6.

This research relies on the TSA method and defines the boundaries of tourism carbon emissions accounting based on tourism consumption-related carbon emissions in industries that provide tourism-related products and services. This study specifically assumes the perspective of consumption allocating to achieve tourism carbon emissions accounting. First, TSA:RMF 2008 presents internationally acknowledged standards for tourism industrial division; the document thus enables scientific division of the industry and ensures a comprehensive accounting scope. Second, the document includes tourism supply and consumption data from the TSA to calculate the tourism proportion and tourism stripping coefficient. By stripping the output generated by tourism consumption from tourism-related industries, TSA:RMF 2008 guarantees the accuracy of data sources. Last, tourism ecological efficiency data are used to convert tourism consumption data into carbon emissions data to realize tourism carbon emissions accounting. The accounting analysis framework is detailed in Figure 2.

Figure 2.

Analysis framework of tourism carbon emissions accounting based on TSA.

The comprehensive decomposition accounting method based on TSA

Given the above analytical framework and in accordance with the main logic of “accounting basics–key coefficient–accounting objective,” this paper establishes an accounting method (see Figure 3) tied to tourism consumption based on the TSA. After scientifically dividing the tourism industry, this paper focuses on computing the tourism proportion and tourism stripping coefficient and then on stripping tourism consumption from the total tourism industry output.

Figure 3.

TSAs-based comprehensive decomposition accounting method for tourism carbon emissions accounting.

STEP 1:

Three-level “sector–industry–product” division of tourism industry

The TSA’s tourism industry classification system is built on tourism characteristic products (TCPs) and tourism characteristic activities (TCAs). TCPs refer to goods or services whose consumption volume declines sharply when the number of tourists plummets in a region; TCAs encompass tourism-related economic activities. TSA:RMF 2008 divides the tourism industry into 12 TCPs and 12 TCAs (i.e., tourism industries). In light of this international standard, and in referring to the System of National Accounts 2008 (SNA, 2008) and International Recommendations for Tourism Statistics 2008 (IRTS, 2008), the naming and relevant expressions of specific sectors are amended based on the three-level “sector–industry–product” division. The resultant tourism industry division structure (see Table 3) comprises seven tourism sectors (accommodation, catering, transportation, tourism, entertainment, shopping, and “others”), 13 tourism industries, and 21 tourism characteristic products (services). Among them, the local tourism entertainment industry (falling under the entertainment sector) includes tourism entertainment services with regional characteristics based on features of the accounting region, such as hot spring services, golf course services, and medical cosmetology services. The “others” sector consists of industries that provide auxiliary products and services to the six identified sectors. Tourism-related services with local nuances can then be practically tailored to the accounting area (e.g., conference and exhibition services; rental services for daily-use equipment).

STEP 2:

Accounting for tourism stripping coefficient

The data demand for accounting tourism carbon emissions can be immediately satisfied if a nation or region has established TSA. In this instance, the tourism consumption data required for accounting can be obtained directly from the TSA. However, the TSA databases for many nations or regions are not actually updated in real-time annually (probably as TSA is difficult to complete). If we want to account for the tourism carbon emissions in consecutive years, we will run into the issue of insufficient data. To solve this problem, we can utilize the total tourism revenue data from the Statistical Yearbook to calculate the actual tourism consumption. Specifically, we use the TSA data for 1 year as the baseline data. First, because the Government Statistics Department does not divide tourism revenue by sector within total tourism revenue data in the Statistical Yearbook, it is necessary to pinpoint the percentage of tourism revenue in the seven sectors overall (i.e., the tourism proportions of seven sectors, α_i) to determine sectors’ tourism revenue (consumption). We can calculate α_i according to the consumption data of tourism industries summarized by sector from the tourism consumption data in Table 6 (i.e., total domestic supply and internal tourism consumption) of the TSA. Second, because the total tourism revenue data of seven sectors from the Statistical Yearbook do not solely entail tourism consumption, the share of output generated by tourist consumption must be distinguished from these sectors’ total output; that is, the tourism stripping coefficient (R_i) must be determined (Wu et al., 2019). Based on the method recommended by the TSA and by taking the ratio of tourism consumption to tourism supply as the tourism stripping coefficient, the data sources for calculating R_i are also from the TSA data for 1 year, the following equation applies:

R_{i} = \frac{T T C_{i}}{T T S_{i}} \times 100 %

(1)

where R_i is the tourism stripping coefficient of sector i; TTC_i represents the total tourism consumption of sector i; and TTS_i is the total tourism output of sector i; and i = 1, 2, …, 7, corresponding respectively to the accommodation, catering, transportation, sightseeing, entertainment, shopping and others sector.

It is possible to calculate the actual tourism revenue of the seven sectors using the Statistical Yearbook data based on the above two coefficients. Specifically, the tourism proportion (i.e., α_i) can first be used to calculate each sector’s tourism revenue in relation to total tourism revenue. Next, the tourism stripping coefficient (i.e., R_i) can be used to compute each sector’s actual tourism revenue. This actual tourism revenue represents the sector’s actual tourism output, with accompanying tourism carbon emissions computed based on the sector’s actual tourism revenue as follows:

T T S_{i} = T T R \times α_{i}

(2)

A T R_{i} = T T S_{i} \times R_{i}

(3)

where α_i is the tourism proportion of sector i, representing the percentage of sector i’s tourism consumption among total regional tourism consumption; TTR represents the total tourism revenue; α_i represents the tourism proportion of sector i; R_i is the tourism stripping coefficient of sector i; and ATR_i is the actual tourism revenue of sector i, that is, TTCi in equation (1).

STEP 3:

Accounting of tourism carbon emissions

While the sector’s actual tourism revenue is the source of tourism carbon emissions, the revenue reflects consumption data rather than carbon emissions data or energy consumption data. Per the cost accounting principle, assuming that the unit benefit costs of the same sector in the production and operation processes are basically consistent, we converted consumption data into carbon emissions data based on tourism ecological efficiency. The interaction between actual tourism consumption and tourism-related environmental costs (i.e., carbon emissions) can then be determined. Ecological efficiency covers ecological environmental efficiency and economic development efficiency. The most popular formula is from the World Business Council for Sustainable Development: [Ecological Efficiency = Product or Service Value/Environmental Impact]. This research refers to the definition and model of ecological efficiency in using tourism ecological efficiency (i.e., TEE) to analyze the correlation between tourism carbon emissions and tourism earnings. Results represent the unit energy consumption or unit carbon emissions of actual tourism revenue and reflect the overall energy/carbon emissions efficiency of economic activities (Zha et al., 2020), written as follows:

T E E_{i} = \frac{A T R_{i}}{T C_{i}}

(4)

where TEE_i represents the tourism ecological efficiency of sector i; ATR_i represents the tourism revenue of sector i; TC_i represents the tourism carbon emissions of sector i (unit is yuan/kg CO₂-e).

Finally, the accounting equation can be applied to identify the carbon emissions of the seven focal sectors. The total tourism carbon emissions can be obtained after weighting:

T C_{i} = \frac{A T R_{i}}{T E E_{i}}

(5)

T C = \sum T C_{i}

(6)

where TC is the total tourism carbon emissions.

Based on the sector’s tourism carbon emissions and the subsector’s tourism proportion, the tourism carbon emissions of each product/service can be given as follows:

T I C_{n} = T C_{i} \times β_{n}

(7)

where TIC_n represents the tourism carbon emissions of the n-th tourism product/service; TC_i is the tourism carbon emissions of sector i; β_n is the proportion of n-th tourism products/services in corresponding tourism sector; and n is the tourism product (service) distinguished in Table 3.

Empirical analysis

This empirical analysis focuses on Guangdong province to test the proposed tourism carbon emissions accounting analytical framework and method based on consumption stripping. Guangdong, a province in southern China, is renowned for its tourism industry; its tourism industry has always been at the forefront of China both in the total tourism revenue and the total number of tourists in the 40 years since China’s reform and opening up (Hu, 2018; Liu et al., 2019). Guangdong was chosen for analysis for several reasons, the first of which was data availability. As one of the earliest Chinese provinces to establish the TSA and accompanying data analysis system, Guangdong provides highly accessible research data. Second, academic disputes over the province’s actual tourism carbon emissions must be reconciled; findings regarding Guangdong’s tourism carbon emissions measurement outcomes have been inconsistent. For instance, Hu (2018) and Liu et al. (2019) both focused on tourism energy consumption to assess the province’s tourism carbon emissions in 2010 based on three sectors: transportation, accommodation, and sightseeing. Hu’s (2018) calculation was 1.5208 million tons, whereas Liu et al. (2019) computed 4.4530 million tons-a roughly threefold discrepancy, because they used different energy consumption coefficients, leading to great differences in their results. A reassessment of carbon emissions in 2010 from the perspective of tourism consumption maybe can resolve the debate.

Data sources

Data were drawn from the Guangdong TSA (GDTSA) in 2010 and the Guangdong Provincial Statistical Yearbooks between 2010 and 2020. The GDTSA is a provincial TSA compiled in accordance with the development of the province’s tourism industry. Taking TSA: RMF 2008 as the compilation standard, the GDTSA has compiled nine account tables containing data on provincial tourism expenditure, tourism consumption, and tourism employment (see Table 4). Provincial statistics and survey data for 2010 were combined for systematic accounting analysis to clarify tourism’s role in Guangdong’s economy. Tourism industries infused with local provincial features were considered separately. The GDTSA provides useful information on Guangdong’s sustainable tourism development in addition to offering theoretical support for devising relevant strategies and policies. This study selected 2010 as a baseline because the data from this year were highly comprehensive. To compile the 2010 GDTSA, Guangdong Tourism Bureau surveyed thousands of large and small tourism enterprises in the province, and all TSA tourism industries were effectively covered, leading to robust and complete data.

The results of Guangdong tourism carbon emissions and analysis

Tourism industry structure and two key coefficients

Three local tourism characteristic industries of Guangdong: The province’s hot spring resort industry, golf course service industry, and conference and exhibition industry fall under tourism and possess local characteristics due to the abundance of hot spring resources, the robust market demand for vacation and leisure, and the active conference and exhibition activities represented by China Import and Export Fair (i.e., the Canton Fair). Hot spring resort services and golf course services are listed as “11. Local tourism entertainment industry” under the entertainment sector in Table 3; conference and exhibition services are listed as “13.5 Tourism-related services with local characteristics.” Guangdong province’s tourism industry consists of 7 sectors, 13 industries, and 22 characteristic products (services) overall.

Two key coefficients of Guangdong’s tourism carbon emissions: The production data from Table 5 of the GDTSA in 2010 and supply and consumption data from Table 6 of the GDTSA in 2010 were combined to construct a tourism sector input–output table. This table summarizes total tourism consumption and supply at the regional level. The tourism proportion and the tourism stripping coefficient can be calculated using the preceding equations (see Table 4).

Tourism carbon emissions structure in 2010

Tourism consumption structure of Guangdong in 2010: By calculation, Guangdong’s actual total tourism revenue in 2010 totaled 182.261 billion yuan, each of the seven chosen sectors was as follows: 28.087 billion yuan for accommodation, 67.557 billion yuan for catering, 49.580 billion yuan for transportation, 8.555 billion yuan for sightseeing, 24.159 billion yuan for entertainment, 3.887 billion yuan for shopping, and 0.436 million yuan for others. It is clear that the three sectors that tourists consume most are catering, transportation, and accommodation. Among them, the contribution ratios of catering reached 37.07%, which is the primary source of tourism consumption. Guangdong province is in fact hailed as the “city of gastronomy”; Cantonese delicacies, as represented by Cantonese cuisine, attract a large number of tourists. This reputation could explain why tourists often spend more on catering when visiting the province (Cohen and Avieli, 2004; Guan and Jones, 2015).

The distribution structure of tourism carbon emissions: Given the practical conditions of Chinese provinces and in line with existing research (Zhen, 2014), the tourism ecological efficiency of Guangdong’s tourism-related accommodation, catering, transportation, sightseeing, entertainment, and shopping sectors in 2010 was calculated to be 19,100 yuan/ton CO₂-e, 70,700 yuan/ton CO₂-e, 49,700 yuan/ton CO₂-e, 257,100 yuan/ton CO₂-e, 188,200 yuan/ton CO₂-e, and 141,900 yuan/ton CO₂-e. The “others” sector spans the whole journey; the remaining six departments are thus well positioned to provide tourism-related products and services. Therefore, tourism ecological efficiency denotes the average of the six sectors’ tourism ecological efficiency, which is 121,100 yuan/ton CO₂-e, as shown in Figure 4. As the figure shows, the seven sectors' carbon emissions totaled 1.4721 million tons (accommodation), 956,200 tons (catering), 998,000 tons (transportation), 33,300 tons (sightseeing), 128,300 tons (entertainment), 274,00 tons (shopping), and 3600 tons (others), respectively. Annual tourism carbon emissions of Guangdong equaled 3.6189 million tons. These results are largely consistent with previous work but are distinct in several ways.

Figure 4.

Tourism carbon emissions of Guangdong province by sector in 2010.

Two differences between accounting results and previous studies: First, accommodation, transportation, and catering were the top three contributors in Guangdong, constituting 94.68% of the total. These tourism departments were therefore the main sources of Guangdong’s tourism carbon emissions. Further, different from the literature indicating that most tourism carbon emissions are attributable to transportation, our findings reveal that tourism accommodation-related emissions accounted for a sizable percentage (as high as 40.68%) of Guangdong province’s carbon emissions-much more than the proportion caused by tourism transportation. Second, other tourism-related activities such as catering, entertainment, and shopping have long been regarded as low-carbon sectors. These sectors are often excluded from estimations of tourism carbon emissions. Yet their emissions were as high as 1.1119 million tons in this study, accounting for 30.72% of the provincial total. The energy consumption and environmental effects of these sectors thus should not be ignored.

The carbon emissions distribution structure across tourism sectors in 2010

To utilize the proportions of each tourism product/service in the associated tourism sector to calculate their carbon emissions, which can further analyze structural differences in carbon emissions across the tourism sectors of Guangdong. Figure 5 depicts the carbon emissions distribution structure of Guangdong in 2010. For the accommodation sector, carbon emissions from hospitality and other accommodation services totaled 1.2638 million tons, representing 85.85%. For the transportation sector, carbon emissions were mainly derived from the road passenger transport industry and air passenger transport industry, collectively accounting for 83.13%. Regarding the sightseeing sector, natural ecological protection and scenic area management services were its main carbon emissions sources. Sports and entertainment services were the largest sources of carbon emissions in the entertainment sector, reflecting the comparative advantage of Guangdong’s sports and recreation industry. Within the “others” sector, carbon emissions from conference and exhibition services equaled 1200 tons (33.33% of the sector total). Besides, the total carbon emission of Guangdong’s three local tourism characteristic industries is 0.6 million tons is a small percentage.

Figure 5.

Tourism carbon emissions structure of Guangdong province in 2010.

Tourism carbon emissions trend from 2010–2021

We further apply the method described above to examine Guangdong’s tourism carbon emissions between 2010 and 2020. Figure 6 portrays the steady growth of tourism carbon emissions following the rapid development of Guangdong’s tourism industry. The result shows that, First, total tourism carbon emissions in the province in 2019 reached 14.3801 million tons, a roughly fourfold jump versus 2010; 91.5543 million tons of carbon emissions were discharged through tourism over merely a decade. If no effective measures are adopted to curb these emissions, then Guangdong’s tourism industry will face tremendous pressure around energy conservation, emissions reduction, and carbon neutralization. Second, regarding the sector structure of tourism carbon emissions, such emissions tied to accommodation, catering, and transportation respectively rose from 1.4721 million tons, 0.9556 million tons, and 0.9976 million tons in 2010 to 5.8475 million tons, 3.7997 million tons, and 3.9669 tons in 2019. These three sectors have continued to generate the most carbon emissions in the province’s tourism industry; they are also targets for future energy conservation and emissions reduction efforts under the local government. Third, COVID-19 directly led to an exponential decline in tourism carbon emissions: Guangdong’s tourism-related carbon emissions plummeted to 4.4593 million tons in 2020 as the cessation of domestic and inbound tourism severely constrained tourism consumption. Yet the growth trend of tourism carbon emissions over the past decade suggests that, as tourism recovers from this slump, energy conservation and emissions reduction will remain focal points of the post-pandemic tourism industry.

Figure 6.

Tourism carbon emissions in Guangdong province, 2010–2020.

Discussion and implications

The main reasons for the differences between accounting results and previous studies

Firstly, why does the accommodation sector generate more carbon emissions than the transportation sector in Guangdong? Based on the sample survey of tourists conducted by the Guangdong Provincial Tourism Bureau in 2010, only 48.43% of tourists were from other provinces. Most came from Hunan, Guangxi, Jiangxi, Fujian, and other provinces around Guangdong, which can arrive in Guangdong within 1–3 hours by high-speed train. Therefore, more than 70% of tourists from other provinces chose to travel by railway, the advances in technology and China’s high-speed railway network have also shortened tourists’ transportation time and corresponding energy consumption dramatically¹; only 17% opted to travel by air transportation. Tourists thus spent little money on air transportation, the travel mode that consumes the most energy in tourism transportation. In addition, improved living conditions have also led tourists to hold higher accommodation standards than before; hotels, especially starred hotels, are expected to devote more attention to satisfying tourists’ needs. For example, linens are replaced daily, air conditioning is automatic, and lights remain on, etc. Tourism accommodation-based energy consumption is accordingly high.

Secondly, why are the carbon emissions from catering, shopping, and entertainment in Guangdong more significant? The results of industry surveys and the practical circumstances in Guangdong show tourism catering to be a main contributor to tourism energy consumption. The higher unit energy consumption of catering is due to the traditional cooking style of Cantonese cuisine (one of China’s four classic cuisines) in Guangdong compared with other provinces and cities. This higher unit energy consumption has also led to higher carbon emissions from tourism catering. Entertainment-related carbon emissions mainly come from theme park activities² and sports entertainment activities,³ both of which draw tourists and cover a large activities scale. These industries’ ongoing development has elevated their contributions to overall carbon emissions from the tourism entertainment sector. Besides, Guangdong province also hosts the most developed economy in China and is a key link in the Guangdong–Hong Kong–Macau Greater Bay Area given its adjacency to Hong Kong and Macau. The province has eight Integrated Free Trade Zones and multiple inbound port entries for land, sea, and air transportation. These geographical advantages have spurred regional tourism shopping, hence why the shopping sector is a core contributor to tourism carbon emissions.

A comparative analysis of the accounting results with existing studies

Liu et al. (2019) accounted for Guangdong’s total tourism carbon emissions in 2010 of the three tourism sectors of transportation, accommodation, and sightseeing is 4.4530 million tons. In contrast, we also account for the total tourism carbon emissions of these three tourism sectors is 2.534 million tons, nearly 2 million tons less than Liu’s result. The reason for this is mainly due to differences in accounting principles and methods. Specifically, Liu’s tourism energy consumption view failed to consider actual tourism output and produced accounting results with statistical aggregate indices of anticipated tourism outcomes. We instead assumed a tourism consumption perspective and adopted secondary stripping; this approach enabled us to fully consider the tourism proportion and the tourism stripping coefficient to strip actual tourism output from relevant sectors. Actual tourism carbon emissions were then calculated to obtain more accurate accounting results. Comparing the results with Liu’s work further implies that the accounting results of our research are relatively scientific and accurate.

Additionally, comparing the results with Liu’s work, the results computed using the accounting framework and procedures developed from the perspective of tourism consumption in the study have another advantage compared with the tourism energy consumption perspective, which can more accurately reflect the destination’s actual tourism carbon emissions. As we all know, different destinations have different main tourist attractions and core tourism industries. For instance, Guangdong Province is famous for its local food, Hong Kong is famous for its shopping industry, and Macau is famous for its entertainment industry. When we account for the tourism carbon emissions for different destinations, we should not only account for the three tourism sectors of tourism transportation, accommodation and sightseeing, we should take the advantageous industries or special industries of the destination as the vital accounting object for consideration. Take Guangdong Province, the case study, as an illustration. Because the region is well-known for its local cuisine, one of the main reasons visitors come is to sample Cantonese food. Our research results from the perspective of tourism consumption found that visitors spent the most on catering in 2010 (118.095 billion yuan), the highest amount among the seven tourism sectors, and as a result, catering’s carbon emissions in that year totaled 956,200 tons or 26.42% of the province’s total carbon emissions from tourism. This accounting outcome is consistent with Guangdong’s current reality. If we use the traditional accounting method, the carbon emissions of the catering sector will not be included in the accounting scope, not to mention the carbon emissions of three local tourism characteristic industries of Guangdong. Therefore, the accounting results can more genuinely and objectively suit the actual situation of the destination by accounting for tourist carbon emissions from the standpoint of tourism consumption based on our study.

Conclusions

Based on the TSA and the logic of “accounting basis–key coefficient–accounting objective,” this paper presented a comprehensive decomposition accounting method from the perspective of consumption stripping. This method simplifies the process of converting tourism consumption data into carbon emissions data and enables the decomposition of the carbon emissions structure within the tourism industry. The approach thus addresses research gaps while enabling more precise accounting of such emissions. Specifically, TSA:RMF 2008 was taken as the theoretical and methodological foundation for scientifically dividing the tourism industry, thereby enabling accurate results. A tertiary tourism industry division structure of “sector–industry–product” was then established. Tourism industries (products and services) with local characteristics were defined along industrial and product dimensions; the suggested industrial division structure is thus applicable to regions with diverse degrees of tourism development. Stripping the tourism consumption tied to tourism activities is paramount to reliable accounting. We next calculated the tourism proportion to identify the proportions of tourism sectors among total regional tourism revenue. The tourism stripping coefficient was then computed to determine the actual tourism output across sectors. This two-part stripping procedure ensured accurate accounting data. Finally, tourism ecological efficiency was used to convert tourism consumption data into tourism carbon emissions data to realize the accounting objective. The industrial distribution structure of carbon emissions was obtained by decomposing the accounting results.

We computed the tourism carbon emissions of Guangdong province from 2010 to 2020 as an empirical case study. Our TSA-based findings (from the tourism consumption perspective) outperformed those from a traditional energy consumption perspective in both accuracy and precision. The accounting results also revealed that accommodation, catering, and transportation contributed most of the tourism industry’s carbon emissions in Guangdong. These findings offer a more holistic understanding of Guangdong’s tourism carbon emissions distribution structure. Results also point to key areas for future carbon emissions control. Such insight presents guidance for establishing energy-saving and emission-reducing measures and for decomposing the main tasks involved in energy conservation and emissions reduction.

As the consequences of carbon emissions caused by human production activities continue to threaten the global climate (Intergovernmental Panel on Climate Change, 2021), the green economy has emerged as a major development goal and mode in the world’s economy (D’Amato and Korhonen, 2021). Tourism carbon emissions accounting is the premise of sustainable tourism development (Tang and Ge, 2018). Energy saving and emissions reduction in tourism, based on carbon emissions accounting, are imperative for transforming tourism into a sustainable service industry. Therefore, the accounting method system developed in this paper carries positive implications for similar Chinese provinces with an established TSA (e.g., Zhejiang, Beijing, Jiangsu, Shandong, and Haikou) as well as for their TSA-equipped international counterparts-currently more than 80 countries or regions. The system can help to promote sustainable tourism development in these regions.

However, there are several limitations to this article. For example, for nations/regions without the TSA, the applicability of this accounting method will be somewhat compromised. This obstacle especially applies to the two key coefficients: extensive market research may be required to collect information on tourists’ consumption data to clarify their tourism expenditure. The tourism proportion and tourism stripping coefficient can then be computed. In addition, because the data on tourism eco-efficiency from other years were not as detailed, hence our decision to take 2010 data as a baseline; and the sectoral tourism eco-efficiency also lacks a globally consistent standard (the standard varies slightly by region), accounting results will differ accordingly. These areas need to be further investigated in the future.

Footnotes

Acknowledgment

We are extremely grateful to the all editors and reviewers, for their valuable comments on an earlier version of this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Natural Science Foundation of China (72074233; 41471467).

ORCID iDs

Fangming Qin

Gang Li

Notes

Author biographies

Fangming Qin received his PhD degree in the School of Business, Sun Yat-Sen University (SYSBS). His research interests include ecotourism, tourism management, and tourism sustainability. His e-mail address: qinfm@mail2.sysu.edu.cn

Jingyan Liu (corresponding author) is professor of School of Business, Sun Yat-Sen University (SYSBS). Her research interests include ecotourism, tourism sustainability, and management of the service industry. She serves as the associate editor for International Journal of Contemporary Hospitality Management. She also serves as the former board member of APac-CHRIE. Her e-mail address: mnsyan@mail.sysu.edu.cn

Gang Li is professor of Tourism Economics in the School of Hospitality and Tourism Management, University of Surrey. His research interests include tourism economic impact, tourism demand forecasting, and tourism sustainability. He has worked with a number of international organizations such as the World Travel and Tourism Council and the Pacific Asia Travel Association on various research projects. He serves as an associate editor for Tourism Management and Annals of Tourism Research. He is a Fellow of the International Academy for the Study of Tourism. His e-mail address: g.li@surrey.ac.uk

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